Socio-Economic Impact of Ebola Virus
Disease in West African Countries
A call for national and regional containment,
recovery and prevention
United Nations Development Group (UNDG) – Western and Central Africa
February 2015
©United Nations Development Group – Western and Central Africa
Cover photo: A school official takes a pupil’s temperature using an infrared digital laser
thermometer in Lagos, Nigeria. Photo by Reuters/Akintunde Akinleye.
Design, layout and production by Camilo Salomón – www.cjsalomon.com.
Printed on environmentally friendly paper (without chlorine) with vegetable-based inks.
The printed matter is recyclable.
Table of contents
Executive summary
ii
Forewordviii
Acknowledgements x
List of figures
xi
List of tables
xiii
Acronyms and abbreviations
xiv
1.Introduction
1.1 Context 1.2 Objectives 1
1
4
2. Overview of the magnitude and dimensions of Ebola virus disease in West Africa
2.1 The EVD in West Africa: Trends, magnitude and dimensions
2.2The EVD in West Africa: Gender and children dimensions
2.3 Regional and continental responses
2.4 The international communities’ response to Ebola Virus Disease
2.5 UN system response to the Ebola Virus Disease
7
7
22
27
28
30
3. The macro-economic impact of the Ebola virus disease 3.1 Overview of relevant pandemic modelling approaches
3.2Methodology
3.3 Key findings from empirical results
37
37
38
39
4. The socio-economic impact of the Ebola virus disease 4.1 The anthropological dimension
4.2 Impact on poverty
4.3 Food security impact of the Ebola virus disease
51
51
56
62
5. Conclusions and policy recommendations
69
References79
Annexes83
Annex 1. The non-linear two-stage least square techniques
83
Annex 2. Methodology of the EVD probability estimation
84
Annex 3. Modelling food security and poverty incidence
86
Annex 4. Variables and data
88
Annex 5. The two sector model
89
Annex 6. Distribution of Ebola virus disease prevalence probability for West Africa countries, 2013-2017
92
Annex 7. Macro model estimation results 94
Annex 8. United Nations Development Group – Western and Central Africa (UNDG-WCA) Regional Directors Team (RDT)
95
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
i
Executive summary
This report is unique. It is the first report to
undertake an assessment of the Ebola virus
disease (EVD) for each of the 15 West African
countries, breaking away from the tradition of
focusing on the three epicentre countries. It is
also the first to assess the impact of EVD on
poverty incidence and food security in both the
three epicentre countries and other non-West
African countries. The estimation approach of
the socio-economic impact, which allows for
consistency checks, is also different from that of
other studies.
The report emphasizes the imperatives of a
regional dimension. With the intensity of the
pandemic in Guinea, Liberia and Sierra Leone, and
even if it is restricted to these three most affected
countries, a long-term propagation of EVD in
these countries will have a substantial impact
on all West African economies. The disease is
unprecedented in scale and virulence. The intensity
and complexity of the outbreak in the three
countries makes it difficult for individual countries
to handle, requiring a coordinated approach. The
relatively free movement of goods and people in
the region and close community ties, which make
it very difficult to contain the outbreak as well as the
limited internal capacity to cope with the outbreak
in terms of human, financial, operational and
logistics capacity calls for a regional dimension. If
the outbreak of EVD is not addressed collectively,
the start of the domestication and implementation
of the Sustainable Development Goals (SDGs) in
these countries and their neighbours will be put at
risk. A regional approach provides an opportunity
to raise awareness on the socio-economic impacts
of Ebola at the regional level and in each of the
15 countries in West Africa. Analysis of the shortand medium-term costs of the Ebola epidemic
should spur governments of non-affected countries
to act quickly to put in place preventive measures.
ii
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
The 2014 Ebola virus disease (EVD) outbreak
in West Africa is the longest, largest, deadliest,
and the most complex in history. Unlike past
outbreaks, which lasted for a very short time, this
outbreak has lasted for more than a year – and
has not yet fully abated. As of 11 February 2015,
there were 22,859 EVD cases and a total of 9,162
deaths. Compared to the cumulative sum of past
episodes in 32 years (1976-2012) – 2,232 infected
people and 1,503 deaths – there are now over ten
times the total number infection cases and over
six times the total number of fatalities. In less than
six months, what started as a public health crisis
in Guinea had degenerated into development
crises (i.e. economic, social, humanitarian and
security threats) in Guinea, Liberia and Sierra
Leone. In contrast with past outbreaks, which
were mostly restricted to remote areas, the West
African case is complex and geographically
widespread, and involves small rural and large
urban centres including Conakry, Monrovia, and
Freetown. Due to the multi-country outbreaks
occurring simultaneously, this pandemic is very
difficult to contain. The infection of 830 health
workers, of whom 488 died, further complicated
its containment. This is the first time that the EVD
has been transmitted to other countries through
air travel.
Several factors make the containment of the
pandemic very difficult. The health systems in
Guinea, Liberia and Sierra Leone were unprepared
for Ebola at the onset of the epidemic. They lacked
sufficient amounts of all that is required to contain
the epidemic: drugs, ambulances, facilities, trained
health personnel, and many other items. Moreover,
impoverished rural areas have more limited
access to services than relatively well-off urban
areas. The protracted civil wars in Liberia and
Sierra Leone, and the intense political instability
compounded the weak health and physical
infrastructure. The inequitable distribution of
human and financial resources has hampered
the response to the epidemic. Due to ignorance
or lack of knowledge and preparedness, health
professionals misdiagnosed the EVD because its
early symptoms resembled those of other diseases
endemic to the region such as malaria, cholera and
Lassa fever. In addition, some people thought that
the disease was being spread by the government
resulting in underreporting and thus contributed
to the silent spread of the virus, which remained
hidden and eluded containment measures. Fear
spreads as fast and wide as a virus. The high
mortality rate associated with Ebola threatens the
performance of many interventions that could
help contain the epidemic. Indeed, due to fear of
infection, the public was reluctant to engage in
contact tracing; infected persons were hesitant
to present themselves for treatment; and health
workers were frightened to provide care. This was
further complicated by the loose migratory pattern
in the region and risky cultural practices. The
longstanding cultural practices that people were
understandably reluctant to abandon contributed
to the further spread of infection. Due to the
culture of burying the dead near their ancestors,
corpses were transferred long distances, which
thereby fuelled new outbreaks. When people
thought that their social, cultural and economic
rights were being violated, they often resorted to
physically assaulting health workers. The difficulty
of coordinating Ebola-related aid and of treating
infected patients using existing infrastructures is
another impediment to stopping the epidemic.
Most other countries are in the same health sector
conditions as those in Guinea, Liberia and Sierra
Leone. The countries are not prepared for any
serious public health crisis like the EVD outbreak;
this calls for a regional approach to preventing
EVD in the future.
EVD does not respect age. All age groups are
affected, but the heaviest toll is on the most active
segment of the population (15-44 years) – the
labour force. This has serious negative implications
on the labour market and national productivity.
The toll is also heavy on children. Around 20
percent of the infected cases are children. Over
16,600 children either lost one or both parents
to EVD, which makes them more vulnerable to
poverty. They lost school hours, ranging from 486
hours in Guinea and 780 hours in Sierra Leone.
There is a feminization of the EVD, and the
disease’s impact is more on women than men
in the three epicentre countries. As of 7 January
2015, the number of EVD cases was higher among
women (50.8%) than among men (49.2%) in
the three epicentre countries. On per 100,000
population, women are more affected than men
(118 against 115). The gender disparity is more
pronounced in Guinea and Sierra Leone. As
care providers, women are more likely to be
exposed to the disease transmission vectors such
as vomit or other bodily fluids of an infected
family member. Furthermore, certain traditional
practices and rituals performed on the deceased
mostly by women can also pose an increased risk.
Women’s access to non-Ebola-related services
has been constrained. For instance, in Sierra
Leone, the number of women giving birth in
hospitals and health clinics has dropped by 30
percent. In addition to being physically affected
by the epidemic, women have suffered reversals
in economic activities and empowerment, due to
EVD control measures that restrict the movement
of people and goods. Women in the three
countries are disproportionately clustered in the
least productive sectors, with 90 percent employed
in the informal services and agricultural sectors.
The EVD has increased their vulnerability to the
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
iii
loss of livelihoods and incomes. The financial
capital of women’s savings and loans groups in
these countries, especially in Liberia and Guinea,
has also been reduced.
The pandemic has threatened the social fabric
that glues society together. The pandemic and
the associated hardships have changed people’s
consumption habits; many have had to eat less
than before the EVD outbreak. There is evidence
that the EVD is eroding the age-long communal
behaviours of the people including attendance at
ceremonies, adjustment in burial rights and less
caregiving to family and community members.
Feelings of distrust between communities and
between the people and their governments
are still strong. The health system had been
weakened in terms of access to health services
including non-Ebola-related services such as
family planning, pre- and post-natal services,
antiretroviral therapies and treatment of endemic
diseases in the region such as malaria and cholera.
Most people in the epicentre countries expressed
a fear for the future of their family, community
and for the whole country. The recovery and
future preventive measures must take cognizance
of people’s feelings and expectations. Building an
enviable social contract between citizens and their
governments should be a priority in the recovery
interventions. An effective management of the
recovery process will help build people’s trust and
confidence and also boost their expectations for
the future.
EVD is pushing people into poverty and making
them more vulnerable. With the brilliant
economic outlook of the past few years, and given
the growth elasticity of poverty for Guinea, Liberia
and Sierra Leone, the incidence of poverty should
be 49.78 percent in Guinea, against 31.20 percent
iv
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
in Sierra Leone and 63.47 percent in Liberia in
2016. However, EVD seems to have reversed this
trend. Compared to baseline, incidence of poverty
is expected to worsen. For Guinea, the rise in
poverty could range from 2.25 percent in 2014
to 7.9 percent in 2015; between 13.8 percent
and 14.1 percent for Sierra Leone; and between
5.5 percent and 17.6 percent during 2014-2015.
The outlook for 2016 is worse for the three
countries. The poverty impact of EVD on non-EVD
affected countries is also high, especially in Mali,
Senegal, Côte d’Ivoire and Niger. Stigmatization,
which reduces international trade and foreign
investments between these countries and their
main trading and investment partners, as well
as the loss of jobs and livelihoods due to closure
of borders to neighbouring countries to Guinea,
Liberia and Sierra Leone made the poverty
impact high.
Food security impact of EVD is high in affected
and some non-affected countries. The restriction of
movements of goods and services, the quarantine
of communities that are food baskets of the
affected countries, the fear of trading with affected
areas, the closure of borders and international
stigmatization that has raised premiums on ships
berthing in West Africa have affected access to
food. There is a strong correlation between EVD
outbreaks and the prevalence of undernutrition. In
this report, the food security impact is evaluated by
the prevalence of undernourishment measured by
the proportion of the population estimated to be at
risk of caloric inadequacy. Relative to the observed
trends during 1992 and 2012, the prevalence
of undernourishment during 2014-2016 could
increase by 2.8 to 5.3 percent in Liberia; 1.30 to 1.39
percent in Sierra Leone, and 0.49 to 1.72 percent in
Guinea. The EVD has reversed the previous trend
in the projected improvement in food security in
Liberia and Sierra Leone. Among the non-heavily
affected countries, the impact on Guinea Bissau
and Côte d’Ivoire is relatively higher than on
other countries in the region. Increasing access
to food and nutrition, and restoring agricultural
production capacity are essential parts of the
recovery interventions.
The cost of the pandemic on GDP is very high, and
Ebola-free West African countries are not immune
from the devastating effects. The findings reveal
earlier results on the three epicentre countries,
but are more pronounced given the intensified
EVD cases and fatalities. In the medium term
(2014-2017), the gains in economic growth of the
past decade seem to have been reversed. The loss
ranges an annual average of 4.9 percent (low Ebola
scenario) to 9.6 percent (high Ebola scenario)
for Guinea, 13.7 to 18.7 percent for Liberia,
6.0 to 8.0 percent for Sierra Leone. The actual loss
in GDP for the low Ebola scenario is highest in
Sierra Leone (US$219 million), followed by Liberia
(US$188 million) and Guinea (US$184 million).
For the high scenario, it ranges from US$315
million (Guinea) to US$245 million (Liberia),
while Sierra Leone could lose as much as an annual
average of around 7.1 percent between 2014 and
2017. The loss in per capita income is highest in
Liberia. The toll on the GDP is considerable in
the three countries lightly affected by the EVD.
On annual average during 2014-2017 for the low
scenario, the loss ranges from US$81.6 million
(Mali) to US$145.2 million (Senegal) and US$1.4
billion (Nigeria). For the remaining West African
countries that are EVD-free, the loss in the GDP
growth varies from 0.1 to 4 percentage points.
The loss of GDP for the whole region for the low
scenario will be US$3.6 billion on average per year
(i.e. 1.2% of the average GDP of the region). It
could also lose around US$18 per capita per year.
This is a substantial economic loss to a region that
is struggling to catch up with other sub-regions of
the world to translate past growth into improved
living conditions for its people.
The loss in GDP and per capita income of this
magnitude has substantial implications on the jobs
and livelihoods, with a serious negative impact on
households’ survival. Rejuvenating lost livelihoods
through appropriate social protection to farmers
in the upcoming planting season and boosting
microfinance to small-scale enterprises are vital.
Given the fiscal stress associated with the EVD,
priority should be given to efforts to boost the fiscal
capacity of the Governments of Guinea, Liberia
and Sierra Leone, such as providing debt reliefs
and concessional loans. Addressing international
stigmatization that weakens international trade
and foreign investment in West African also
deserves urgent attention.
Ebola is not only a threat to national security,
but also an impediment to sub-regional, regional
and global security, which therefore requires
supranational and global attention. The role
of regional and continental organizations in
fighting the pandemic is yielding some results.
The Mano River Union’s concerted efforts in
calling on the international community to support
their capacity building for surveillance, contact
tracing, case management and laboratory testing
as well as facilitating sharing of information,
expertise and resources among member states
are commendable. The unparalleled efforts of
the Economic Community of West African
States (ECOWAS) Authority in establishing the
Ebola Solidarity Fund, mobilizing experts for the
epicentre countries, and creating the Regional
Centre for Disease Control and Prevention are
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
v
epochal. The African Union’s Support to Ebola
Outbreak in West Africa (ASEOWA), which
has deployed a considerable number of Ebola
volunteer workers (launched by the African Union
in collaboration with Nigeria and Ethiopia), forged
partnerships with the private sectors, especially
telephone service providers, mobilized resources
and mandated the establishment of Africa’s Centre
for Disease Control and Prevention, is laudable.
The United Nations Development Group for
West and Central Africa (UNDG-WCA) calls
for synergy between the regional and continental
disease control centres and commits its support to
their operationalization. Strategies to make them
functionally effective should be put in place.
The international community must fulfil their
pledges and support the establishment of a
mechanism that allows for rapid disbursement.
As of 31 December 2015, only one third of the
pledges was disbursed, and a substantial part
of the disbursement came in October when
the pandemic had already devastated lives and
livelihoods. The slow pace of converting pledges
to commitment calls for immediate correction.
Fulfilling the pledges is vital to ending the
pandemic and accelerating early recovery. The
international community should establish a
mechanism that would allow rapid disbursement
of funds during public health threats like Ebola.
To ensure sustainability, ownership and capacity
strengthening, the international organizations
should work with governments to mobilize
resources for the recovery process.
The global community is ill-prepared for a
devastating pandemic like Ebola, and the next
outbreak should not take the world by surprise.
An important lesson emerging from the pandemic
is that the global community is not ready to
vi
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
address virulent pandemics like Ebola. The global
health governance structures are inadequate, the
international commitment to bolster pandemic
preparedness and response capacity in poor
countries is tardy, and the global support for
strengthening health systems is still weak. This,
therefore, points to the urgent need to reform the
global health management system in order that it
will be able to cope with such pandemics when
they occur. Current partnerships with research
institutions and pharmaceutical companies should
be translated into strategic actions that will lead
to the invention of the requisite vaccine for the
disease within a very short time.
The strategic engagement of the UN System on the
EVD response has enhanced its relevance in the
region, and UN agencies still have a major role
to play in overcoming the outbreak. While most
partners withdrew at the peak of the crisis, UN
agencies not only remained, but also increased
their presence in the epicentre countries and the
corridor countries (Senegal and Ghana). This
helps mobilize partnerships and action as well as
resources for the Ebola response. Consequently, the
UN System became a trusted partner in the region.
Its available resources have been reprogrammed
for Ebola containment and the associated early
recovery efforts. Working with national and
regional institutions to strengthen coordination
mechanisms further aided the response actions
in the region. The United Nations Development
Assistance Framework (UNDAF) for the region
should be concerned with strengthening the health
services’ capacity to cope with future epidemics
without compromising the fight against other
priority diseases, and ensuring the provision of
quality care.
Community Health Center in Freetown, Sierra Leone. The National Ebola Response Centre (NERC) started in Freetown a special operation in the Western Area to control the
current rate of infection and stop the spread of the disease in the region. Photo by UNMEER /Martine Perret
Combined national and regional preventive
mechanisms are imperatives. The containment
of the outbreak is beyond the capacity of a
single country. Complementary actions from
sub-regional, regional, continental and global
bodies are important for maximum success. Both
national and regional preventive measures for the
entire region, especially in countries with a high
probability of EVD occurrence as predicted in this
report report (e.g. The Gambia, Ghana and Côte
d’Ivoire) are needed for enhanced results. This
includes developing early warning plans based on
multidisciplinary approaches and strengthening
the capacity for early reaction and disaster
management system at the national and regional
levels.
A combined strategy works best. Intensifying
contact tracing to remove infected individuals from
the general population, placing them in a setting
that can provide both isolation and dedicated care,
and dealing with associated psychological factors
have proved effective. A densely populated country
like Nigeria used this approach to bring EVD
under control in a very short time. Cross-border
contact tracing is more effective than a single
country contact tracing when EVD occurs in
multi-countries simultaneously.
Learning from the experiences of countries that
succeeded in containing the epidemic is key.
The rapid responses from Senegal and Nigeria
are positive lessons learned. In these countries,
there were competent and relatively adequate
health personnel, decentralized health systems,
community engagement and strong leadership
commitment.
A regional approach to containing EVD will
be more effective than just focusing on national
preventive actions. When there are simultaneous
outbreaks in multiple countries that are contiguous
to each other, joint cross-border contact tracing,
joint treatment and holding centres should
become more effective. National actions then
become complementary. The outbreak in Guinea,
Liberia and Sierra Leone is a warning to others
in the region because the health systems and
their vulnerabilities are the same. EVD does not
know boundaries, and not a single country is
immune from the outbreak. This report calls for
a combined national and regional preventive and
early response mechanisms for West Africa. The
region cannot afford to be unprepared.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
vii
Foreword
This report is a part of the support provided by the Regional United Nations Development Group (UNDG)
to help contain the Ebola Virus Disease (EVD) in West and Central Africa and assist the countries of the
region in recovering from it. This support complements the efforts of national governments, non-state actors
and regional organizations to stamp out the disease. Each of the United Nations agencies involved in the
preparation of the report has contributed its own research and reporting, which has been enriched by recent
recovery assessments conducted by the UN and other partners.
This is the first report to carry out an assessment of the EVD for each of the 15 West African countries, as well
as the first to provide in-depth evidence of the impact of EVD on poverty and food security on the region.
The fight against Ebola in West Africa is showing the limitations of national containment responses, especially
when several outbreaks are taking place in contiguous countries at the same time. Health systems in West
Africa exhibit similar weaknesses to the ones in Guinea, Liberia and Sierra Leone, and the factors that aided
the spread of the disease in the epicentre countries are also present in the wider sub-region. These include the
free movement of goods and people across countries, close ties among border towns, low levels of education,
and limited internal capacity to respond to the outbreak. Tackling Ebola in such circumstances requires
cross-border contact tracing, the creation of joint treatment and other actions to complement national efforts.
Since no country is immune from Ebola, the outbreak in Guinea, Liberia and Sierra Leone should serve as
a warning and a call to action for the wider region. This report calls for combined national and regional
preventive and early response mechanisms for West Africa.
The 2014 Ebola outbreak in West Africa is the longest, largest, deadliest, and the most complex ever witnessed.
As of 11 February 2015, there were 22,859 EVD cases and 9,162 cumulative deaths: this is more than ten times
the total numbers of infected people and over six times the total number of deaths from all previous outbreaks
combined. Unlike past occurrences, which affected remote locations, the current outbreak is geographically
spread out, involving both countryside and cities, and occurring simultaneously in different areas and countries.
Several factors complicate the containment of the pandemic. The health systems in Guinea, Liberia and Sierra
Leone, and in most other countries in the region were unprepared for the Ebola outbreak, lacking trained
personnel, equipment and financing. Impoverished rural areas have specifically suffered, because they have
more limited access to services than relatively well-off urban areas. This inequitable distribution of human and
financial resources has hampered the response to the epidemic. Ignorance, lack of preparedness and fear have
also played an important role. Health professionals have misdiagnosed EVD cases because the early symptoms
of the disease resemble those of malaria, cholera and Lassa fever. Many people have denied the existence of
Ebola or believe it was spread by the government to raise international funds that they will never see. All these
factors have caused the virus to spread silently. Fear spreads as fast and wide as a virus. Due to fear of infection,
members of the public have been reluctant to engage in contact tracing; infected persons are hesitant to present
themselves for treatment; women are giving birth without modern medical attendants; and health workers are
frightened to provide care. This is further complicated by intense migration flows and risky cultural practices.
For instance, many communities insist on burying the dead near their ancestors, moving corpses over long
distances and creating additional risks of infection. In addition, overly centralized health systems have impaired
the engagement of local communities, which is so critical to fighting epidemics such as this one.
viii
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Ebola also takes a higher toll on women and children. Around one fifth of the infected cases are children;
over 16,600 boys and girls have lost either one or both parents to the pandemic, and were unable to go to
school for months. As of January, 2015, around 51 percent of the infected cases were women, representing
118 per 100,000 population against 115 for men. Women’s role as caregivers and their participation in
traditional practices and rituals such as burials make them more vulnerable to contracting Ebola. In
addition, fewer births have been attended by trained medical personnel, and women have suffered reversals
in economic activities due to their large presence in informal activities. To this end, the role of women and
girls in the post-crisis recovery will be essential to facilitate an expedited normalization of the social and
economic landscape.
The pandemic has threatened the social fabric that glues society together in all affected countries – trust
between the people and their governments, and between communities has weakened, and the traditional
social capita of caring family and community members has declined. Most people in the epicentre countries
expressed a fear for the future of their families, communities and countries. The EVD is pushing people
into poverty and making them more food insecure and vulnerable to shocks. The cost of the pandemic on
the economy has been very high, with indications that economies in West Africa have also suffered from
its consequences. Recovery interventions must give priority to addressing these challenges, especially the
creation of jobs, livelihoods and incomes.
Ebola is not only a threat to national security, but also an impediment to sub-regional, regional and global
security. It therefore requires global attention. The Mano River Union (MRU), the Economic Community
of West African States (ECOWAS) and the African Union have taken a regional approach to tackling Ebola
in West Africa. Such an approach will be more effective than just focusing on national preventive actions. A
healthy population is a necessary condition for rapid and sustained growth and development. West African
governments should increase investments in health to accelerate recovery efforts. Strengthening health systems
and addressing the structural vulnerabilities that allowed Ebola to take hold in the first place will help to ensure
that such a crisis will never happen again.
Finally, the strategic engagement of the UN System in the Ebola response has enhanced its relevance in the region.
UN agencies should work with national and regional institutions to strengthen coordination mechanisms for
recovery. The United Nations Development Assistance Framework (UNDAF) for each country in the region
should be concerned with strengthening the capacity of health services to cope with future epidemics without
compromising the fight against other priority diseases, ensuring the provision of quality care, and providing
well-targeted and effective social protection mechanisms that could accelerate recovery.
Abdoulaye Mar Dieye Joséphine Odera Mr. Benoit Kalasa UNDP Regional Director for UNWOMEN Regional Director UNFPA Regional Director Africa and Chair, Regional UNDG-WCA Vincent Martin
FAO Resident Representative
for Senegal & Head of
Regional Office for Resilience,
Urgency and Rehabilitation
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
ix
Acknowledgements
This report is a collective contribution to the Ebola virus disease (EVD) response by the various UN
agencies that make up of the United Nations Development Group (UNDG) for West and Central Africa
(WCA). The report is distinctive because it is the first to assess the socio-economic impact of EVD on
each of the 15 West African countries and offers a regional approach to the pandemic’s containment,
recovery and preventive measures. It was prepared under the strategic guidance of the UNDP Regional
Director for Africa and the Chair, UNDG-WCA, Abdoulaye Mar Dieye. The leadership support from
Joséphine Odera (UN Women Regional Director), Benoit Kalasa (UNFPA Regional Director), Vincent
Martin (FAO Resident Representative for Senegal and Head of Regional Office for Resilience, Urgency
and Rehabilitation) and Ruby Sandhu-Rojon (UNDP Deputy Regional Director).
This report was prepared by a core team led by Ayodele Odusola, Chief Economist and Head, Strategy
and Analysis Team at the UNDP Regional Bureau for Africa, who ensured strategic and policy
coherence of the issues raised in the report. The technical support from Gaston Gohou, Souleymane
Sadio Diallo and Damien Echevin, who explored the macro- and socio-economic aspect of the report,
and Julien Gavelle, who undertook the survey in Guinea and provided inputs on the anthropological
dimension of the report is well acknowledged. Thanks are due to Mensah Aluka (Regional Coordination
Specialist), who facilitated coordination with the various UN agencies and linkages with some
consultants. The smooth facilitation provided by Caroline Mwende Mueke (Special Advisor to the
UNDP Regional Director) is also appreciated. The operational support provided by Luc Gnonlonfoun
(UNDP Operations Manager, Senegal) and his team is deeply appreciated. The technical inputs and
feedbacks from colleagues from UN Women (Peterson Magoola, UN women Deputy Representative in
Sierra Leone; Dr. Alioune Diagne, Monitoring & Reporting Specialist; and Dr. Ndeye Astou Badiane,
HIV/AIDS Programme Officer); UNFPA (Jonathan Budzi Ndzi, Humanitarian Programme Specialist;
and Anandita Philipose, Programme Analyst Ebola Cell); FAO (Patrick David, Regional Food Security
Analyst; and Oriane Turot, Food Security Analyst) and UNDP (Solange Uwera, Coordination Officer,
Rwanda; Sallem Berhane, Policy Specialist, Strategy and Analysis Team; and Stanley Kamara, National
Economist, Liberia Country Office) are appreciated.
The excellent editorial work of Barbara Hall is deeply appreciated. The support from Nicolas Douillet
and Lamin Bal on language and style, and the immense logistical support from Jonas Mantey and
Yechi Bekele are also acknowledged. Many thanks go to the leadership of the Senegal and Guinea UNDP
Country Offices, without which this report would not have been possible.
x
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
List of figures
Figure 1.
The Ebola virus disease epidemic – cases and deaths 9
Figure 2.
Evolution of the epidemic in Guinea, Liberia and Sierra Leone 9
Figure 3.
The Patients Database – Confirmed weekly EVD cases reported nationally, 24 February 2014 to 1 February 2015 10
Figure 4.
New cases over the past 21 days, as of 11 February 2015 10
Figure 5.
The Ebola virus disease: Number of cases, deaths and fatality rates, as of 7 January 2015 11
Figure 6.
No. of cases of the Ebola virus disease, deaths and fatality rates among health workers, as of 11 February 2015
11
Figure 7.
Demographics of those affected by the Ebola virus disease, as of 11 February 2015
12
Figure 8.
Mapping of the Ebola virus disease in Africa 13
Figure 9. Geographical spread of the Ebola virus disease, as of 11 February 2015 14
Figure 10. Cumulative number of cases of the Ebola virus disease in Guinea, Liberia and Sierra Leone, 26
by gender, as of 7 January 2015
Figure 11. No. of learning hours lost due to school closures due to the Ebola virus disease 26
Figure 12. Pledges, commitments and disbursements for the UN Ebola Plan, as of 27 October 2014 30
Figure 13. Guinea: Impact of the Ebola virus disease on GDP growth and GDP per capita growth,
42
2014-2017
Figure 14. Liberia: Impact of the Ebola virus disease on GDP growth and GDP per capita growth, 43
Figure 15. Sierra Leone: Impact of the Ebola virus disease on GDP growth and GDP per capita 44
Figure 16. Senegal, Mali and Nigeria: Impact of the Ebola virus disease on GDP growth, 2014-2017 46
Figure 17. West African countries not directly affected by the Ebola virus disease: Impact of the 47
Figure 18. West African countries not directly affected by the Ebola virus disease: Impact of the 48
Figure 19. West African region: Impact of the Ebola virus disease on GDP growth and GDP 49
Figure 20. Perceptions of changes in the labour market and the overall economy 51
Figure 21. Change in school attendance since the onset of the Ebola virus disease 52
2014-2017
growth, 2014-2017
Ebola virus disease on GDP growth, 2014-2017
Ebola virus disease on GDP growth (%), 2014-2017
per capita growth, 2014-2017
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
xi
Figure 22. Acceptance in adopting or taking care of children whose parents have died of the 52
Figure 23. Changes in the practice of traditional initiation ceremonies 53
Figure 24. Impact of the Ebola virus disease on access to health services 54
Figure 25. Fear for the future, at the family and community, and country levels 55
Figure 26. Change in people’s trust and confidence in the government 56
Figure 27. Poverty impact of the Ebola virus disease in Guinea, Liberia and Sierra Leone 58
Figure 28. Poverty impact of the Ebola virus disease in Benin, Burkina Faso, Côte d’Ivoire 59
Figure 29. Poverty impact of the Ebola virus disease in The Gambia, Guinea Bissau, Mali 60
Figure 30. Poverty impact of the Ebola virus disease in Nigeria, Senegal and Togo 61
Figure 31. The prevalence of undernourishment in West African countries, 1990–2012 62
Figure 32. Impact of the Ebola virus disease on food security in the main epicentre countries 64
Figure 33. Impact of the Ebola virus disease on food security in Benin, Burkina, Côte d’Ivoire 65
Figure 34. Impact of the Ebola virus disease on food security in The Gambia, Guinea Bissau, 66
Figure 35. Impact of the Ebola virus disease on food security in Nigeria, Senegal and Togo 67
Ebola virus disease and Ghana
and Niger
and Ghana
Mali and Niger
xii
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
List of tables
Table 1.
Total number of confirmed and probable cases, by gender and age group, in Guinea, Liberia and Sierra Leone
12
Table 2.
Impact of health system deficiencies on Ebola outbreak containment 17
Table 3.
Infrastructures and development indicators in the epicentre countries 18
Table 4.
Infrastructures and development indicators in non-epicentre West African countries
19
Table 5. United Nations Ebola Response Plan 33
Table 6. Share of countries in intra-ECOWAS trade
40
Table 7. Member Countries’ share of intra-ECOWAS imports and exports in foreign trade 41
Table 8. Guinea: Macro-economic impacts of the Ebola virus disease, 2004-2017 42
Table 9. Liberia: Macro-economic impacts of the Ebola virus disease, 2004-2017 43
Table 10. Sierra Leone: Macro-economic impacts of the Ebola virus disease, 2004-2017 44
Table 11. Senegal, Mali and Nigeria: Macro-economic impacts of the Ebola virus disease, 2004-2017 45
Table 12. West Africa region: Macro-economic impacts of the Ebola virus disease, 2004-2017 49
Table 13. Food security panel data model results 63
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
xiii
Acronyms and abbreviations
AfDB
African Development Bank
CERF
Central Emergency Response Fund
CGE
Computable General Equilibrium
CMC
Case Management Centre
DPT
Diphtheria pertussis and tetanus
DRC
Democratic Republic of the Congo
ECA
Economic Commission for Africa
ECOWAS
Economic Community of West African States
EU
European Union
EVD
Ebola virus disease
FAO
Food and Agriculture Organization of the United Nations
GDP
Gross domestic product
HIV
Human Immunodeficiency Virus
HoS&G
Heads of State and Government
IFAD
International Fund for Agricultural Development
ILO
International Labour Organization
MDG
Millennium Development Goal
MRU
Mano River Union
MSF
Médecins Sans Frontières/Doctors Without Borders
MSWGCA Ministry of Social Welfare Gender and Children’s Affairs
xiv
NGO
Non-governmental organization
OCHA
United Nations Office for the Coordination of Humanitarian Affairs
OHCHR
United Nations Office of High Commissioner for Human Rights
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
RO
Reproductive rate
SDG
Sustainable Development Goal
UN
United Nations
UNDG
United Nations Development Group
UNDP
United Nations Development Programme
UNECA
United Nations Economic Commission for Africa
UNFPA
United Nations Population Fund
UNHCR
United Nations High Commissioner for Refugees
UNICEF
United Nations Children’s Fund
UNIDO
United Nation Industrial Development Organization
UNMEER
UN Mission for Ebola Emergency Response
US$
United States dollar
WCA West and Central Africa
WAEMU
West African Economic and Monetary Union
WFP
World Food Programme
WHO
World Health Organization
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
xv
A family harvesting rice in Gueckedou area. Photo by Julien Gavelle/FAO Guinea (Nov. 2014)
Introduction
1. Introduction
1.1 Context
The Ebola virus disease (EVD) epidemic in
Guinea, Liberia and Sierra Leone is the longest,
largest, deadliest, and the most complex and
challenging Ebola outbreak in history. It is
unprecedented in terms of its duration, size of
infections and fatality, and geographical spread.
Unlike the past outbreaks, which lasted for a
very short time, the West African case has lasted
for more than one year – and has not yet fully
abated. As of 11 February 2015, there were
22,859 EVD cases in total: 3,044 in Guinea, 8,881
in Liberia, and 10,934 in Sierra Leone – with a
cumulative death of 9,162. In fact, in only around
six months, there were 3,774 cases of people
infected by EVD and 1,888 deaths in Guinea,
Liberia and Sierra Leone, which surpassed the
cumulative sum in 32 years (1976-2008) of 2,232
infected and 1,503 deaths. As of December 2014,
the number of Ebola cases in this outbreak is
four times higher than the combined total of all
prior outbreaks.1
What started as a public health crisis in Guinea on
26 December 2013 degenerated into development
crises (economic, social, humanitarian and
security threats) in the three epicentre countries
in less than six months. The deadly disease’s
knock-on effects are huge – loss of lives, stifled
growth rates, reversed recent socio-economic
gains, aggravated poverty and food insecurity,
and destroyed livelihoods, particularly affecting
women and children.
Unlike the past outbreaks, which were mostly
restricted to remote areas, the West African case
is complex; it is geographically widespread,
involving small rural and large urban centres
1
(including Conakry, Monrovia, and Freetown).
The
multi-country
outbreaks
occurring
simultaneously make this pandemic unique.
Due to the high transmission rate and limited
capacity to manage the epidemic at the outset,
as of 7 January 2015, the fatality rate is also high,
from 35.3 percent in Sierra Leone to 64.1 percent
in Guinea, Its spread has been complicated
by health workers becoming infected (830, of
whom 488 died). The loose migratory pattern
in the region, fear, ignorance and risky cultural
practices make the containment of the epidemic
challenging. In addition to spreading to several
West African countries (Senegal, Nigeria
and Mali), the EVD has also been detected in
other parts of the world, including Spain, Italy,
Germany and the United States of America.
This is the first time that EVD is transmitted
to other countries via air travels. It was first
transmitted to Nigeria by a traveller from
Liberia. Within a very short period, it has been
transported to Europe and America. This led to
a pronounced international stigmatization
against West Africa, a development that triggered
risk aversion behaviours, which wreaks havoc
on the region’s economy.
The spreading rate of this outbreak and the
grave need to control the epidemic resulted in
the governments of the hardest-hit countries
to declare a national emergency and the
World Health Organization (WHO) “a public
health emergency of international concern”.
Also, in September 2014, the United Nations
Security Council declared the crisis a “threat
to international peace and security” and
unanimously called for a coordinated approach
in dealing with the outbreak.
For the current situation on the epidemic, see http://apps.who.int/ebola/en/ebola-situation-report/situation-reports/ebola-situation-report-11-february-2015) and for a comparison between
past and recent episodes of EVD, see Salaam-Blyther (2014) and UNDP (2014d).
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
1
Instead of looking at the issue through the lens
of the three epicentre countries, this report
takes a new approach to assessing the impacts
of EVD through a regional perspective. Why
the regional focus? The epidemic is ravaging the
economic and social fabric of the three epicentre
countries (Guinea, Liberia and Sierra Leone), and
the human toll is historically the worst ever. In
addition to these three heavily affected countries,
smaller outbreaks also occurred in Senegal,
Nigeria and Mali. These countries were declared
Ebola-free following drastic measures taken by
governments to contain the disease. This situation
holds considerable uncertainty for the countries
across the region, which is already feeling the
socio-economic repercussions of the epidemic.
The disease is wide in scale and virulent. Without
fast, well-adapted and coordinated responses,
the EVD epidemic rapidly snowballed into
an unprecedented development crisis, with
significant consequences in a region whose
population already has one of the world’s highest
poverty rates and lowest human development
indicators, and is extremely vulnerable to shocks.
Even if the epidemic is contained in the three
most affected countries, a long-term propagation
of EVD in Guinea, Liberia and Sierra Leone will
have a substantial impact on all West African
economies.
The relatively free movement of goods and people
in the region makes it very difficult to contain the
outbreak. For instance, people in Guinea, Sierra
Leone and Liberia, and across borders are able
to move freely due to the long-term communal
relationship, the exceptional porous borders,
and the possibility of using a common Economic
Community of West African States (ECOWAS)
passport in official designated ports of entry.
2
2
Fluid cross-border movement aided the spread
in two ways. First, it has made cross-border
contact tracing very difficult. Second, as the
situation in one country begins to improve, it
attracts patients from neighbouring countries
seeking better health care, which has re-ignited
the transmission chains.
The close community ties and movement
within and across borders of West African
states and the limited internal capacity to cope
with the outbreak in terms of human, financial,
operational and logistics capacity call for a
regional approach. An individual country study
would only show the intensity of the outbreak;
a regional approach would provide a holistic
dimension to the issues, which could provide
guidance in preventing a future occurrence
in the affected and unaffected countries. The
health and sanitation infrastructure in Guinea,
Liberia and Sierra Leone is well below the
average for Africa, especially in rural areas.2
The intensity of the outbreak in Guinea,
Liberia and Sierra Leone makes it difficult for
individual countries to cope, even if there is a
strong internal capacity. The sporadic spread
of the EVD could be likened to the past civil
wars in these countries, which were effectively
countered by regional and international
efforts. The incidence of Ebola has weakened
the highly promising economic recovery
witnessed over the past decade. The emerging
loss of confidence in governments and their
capacity to provide basic services could threaten
social cohesion, which could relapse into the
protracted social and economic abyss of the past
with serious implications on regional stability.
The past conflicts in Liberia and Sierra Leone
This was acknowledged during a two-day emergency meeting convened by the WHO of Ministers of Health of the affected countries and other selected countries and partners, in Accra, Ghana,
from 2 to 3 July 2014. A common strategy was adopted outlining priority actions to halt the spread of Ebola virus in the region.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
are important factors in explaining the limited
progress on the Millennium Development Goals
(MDGs) in these countries. If the outbreak of
EVD is not addressed collectively, the start
of the domestication and implementation of
the Sustainable Development Goals (SDGs) in
these countries and their neighbours will be
put at risk. This, to a large extent, weakens
the initial conditions for these countries to
roll-out the implementation of the post-2015
Development Agenda.
A regional report provides an opportunity to
raise awareness on the socio-economic impacts
of Ebola at the regional level and on each
of the 15 countries in West Africa. Analysis
of the short- and medium-term costs of the
Ebola epidemic should spur governments of
non-affected countries to act quickly in setting
up preventive measures. This would also raise
advocacy on improved coordination as an
important strategy to resolve the health and
economic crises associated with the spread of
EVD in West Africa.
This report is innovative for three reasons. First,
it carries out an assessment of the EVD for each
of the 15 West African countries. According
to the United Nations Development Group
for West and Central Africa (UNDG-WCA),
the recent studies on this topic, for example,
those carried out by the World Bank, the
United Economic Commission for Africa
(UNECA), the United Nations Development
Programme (UNDP) and the WHO, assessed
the impact of the EVD on the three epicentre
countries and to an extent provided a rough
estimate of the impact on the region as a group.
This report assesses the impact. Second, the
report is the first in assessing the impacts
of the EVD on poverty incidence and food
security in all West African countries. Finally,
it uses a macro-economic model that has not
previously been used to assess the impact of
the EVD. Hence, it confirms some of the results
of previous studies (e.g. World Bank 2014;
UNECA 2014; and UNDP-RBA, 2014a). This
report uses a different estimation method and
model from recent reports from World Bank
and UNECA. Its approach is similar to some
extent to that used in UNDP-RBA (2014a).
The results from UNDG-WCA report are
broadly similar, in particular in terms of GDP
growth for the three main affected countries.
In addition to assessing the poverty incidence
and the food security situation in these
countries, this report extends the scope and
covers the 15 West African countries for the
2014-2017 period. Moreover, epidemiological
and economic forecasts continuously change
since the epidemic is still not contained;
hence, it is important to examine potential
responses based on the most recent situation,
which further enhances the contribution of
this report.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
3
1.2 Objectives
The main objective of this report is to evaluate the
macro-and socio-economic impact of the EVD
on each of the 15 West African countries and for
the West African region as a whole. The specific
objectives are as follows:
• Identify the magnitude and dimensions of the
outbreak in the region.
• Assess the economic and social issues that
make the containment of the outbreak
challenging and difficult to manage, including
the overall health system governance structure
and capacity, as well as other policy and
institutional factors that affect the response and
coping mechanisms.
• Assess the immediate and medium-term effects
on economic growth and strategic sectors.
• Examine the socio-economic impact of the
EVD outbreak, including loss in productivity
and jobs, or disruptions of rural and urban
livelihoods, as well as gender dimensions.
• Discuss how the EVD outbreak will impact the
UN agencies’ programmatic engagements in
the affected countries.
• Make necessary policy recommendations
to address the established gaps, losses and
weaknesses at national and regional levels.
4
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
The next section of the report provides an overview
of the magnitude and dimension of the EVD in
West Africa and also presents the response of UN
agencies. Section 3 presents the macro-economic
impacts of the EVD for each of the 15 West
African countries, while Section 4 presents the
socio-economic impacts. The final section presents
the main conclusions and recommendations.
The construction site of the Kaka bridge (in the Coyah District) was stopped and abandoned by DAI Nippon Constructing, who also repatriated workers due to Ebola.
Photo by UN RCO Guinea/Communication Office
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
5
One of the annual festivals, held at the national stadium in Bamako, Mali, January 2015. Photo by UNMEER/Pierre Perron
Overview of the magnitude
and dimensions of Ebola virus
disease in West Africa
2.Overview of the magnitude and
dimensions of Ebola virus disease
in West Africa
This section provides an overview of the magnitude
of EVD and examines key drivers that make it
difficult to contain the crisis. It also provides the
health system and gender dimensions of the EVD
and the UN response to the crisis.
West Africa is experiencing the most important
epidemic of Ebola since the beginning of 2014.
This epidemic that began in the Guékédou region
in Guinea became a sub-regional health problem
whose complexity has undermined the basis for
development in the West African sub-region. Since
the appearance of the disease in the former Zaire
in 1976, this is the first time that it has reached
such proportions, both in geographic scope and
number of victims.
2.1.The Ebola virus disease in
West Africa: Trends, magnitude
and dimensions
The EVD outbreak was first reported in 1976 in
Yambuku, a village in the Democratic Republic
of the Congo).3 Since then, more than 20 Ebola
outbreaks have occurred mainly in East and
Central African countries, including in Gabon
in 1996, where 21 of the 31 cases resulted in
death. According to the WHO, the first-known
infection concerned an 18-month-old-boy who
died on 26 December 2013 in Méliandou, a
remote village located in Guékédou, not far from
the Sierra Leonean and Liberian borders. On 1
February 2014, the virus was carried to Conakry
by an infected member of boy’s extended family.
Between January and March, the epidemic
2
spread to neighbouring areas of Kissidougou
and Macenta, but was formally confirmed only on
21 March 2014.
In the beginning of March 2014, the WHO
reported 29 deaths in 49 cases in Guinea, revised
to 59 deaths per 86 cases two days later. In less
than six months, what appeared to be a contained
crisis in the Guinée Forestière Region spread
across the borders of Sierra Leone and Liberia.
Liberia recorded its first case in Foya District
(Lofa County) on 30 March, and by 2 April, the
virus was transmitted to people in Monrovia.
By late March 2014, the epidemic had spread to
neighbouring Liberia, where 209 confirmed cases
and 131 deaths were recorded on 17 April. The
spread of the EVD outbreak was also observed
on 27 May in Sierra Leone, where it spread
more rapidly than in the other countries. The
neighbouring countries of Senegal and Mail were
also not spared. The virus also spread as far as
Lagos, Port Harcourt and Enugu in Nigeria – the
first transmission via air travel. The EVD was
not limited to the shores of West Africa; it had
also been detected in other parts of the world
including Spain, Italy, Germany and the United
States of America. While the world is grappling
with the current wave of EVD in West Africa, a
different strain of the virus was also discovered
in Jeera County of the Democratic Republic of
the Congo (DRC) in August 2014. But unlike the
West African situation, all the cases are localized
in Jeera County in Watsi Kengo, Lokolia, Boende,
and Boende Muke villages.
As pointed out by Quammen (2014), 280 deaths were recorded out of the 318 cases, i.e. 88 percent.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
7
The rate of increase of the disease has varied
considerably among countries and regions within
individual countries, as well as over time. A single
case of EVD that occurred on 26 December
2013 in a remote village (Meliandou) in Guinée
Forestière led to the infections of 2,599 people
in the three main affected countries (of whom
1,422 died) as at 20 August 2014: 1,082 cases in
Liberia, 910 cases in Sierra Leone and 607 cases
in Guinea. In less than two months (between
20 August and 1 October 2014), the total number
of identified patients more than doubled to
6,553 cases in the three countries: 3,458 cases
in Liberia, 2,021 patients and 1,074 in Guinea.
During this period, the infection rate more
than doubled in Sierra Leone and more than
tripled in Liberia. Subsequently, the situation
worsened considerably in Sierra Leone. For
instance, the infection rate in Sierra Leone as of
20 December 2014 was more than ten times the
cases recorded on 20 August thereby surpassing
the rate of infection in Liberia. By 11 February
2015, a total of 22,859 infected cases and 9,162
deaths had been recorded (WHO, 2015). Sierra
Leone had the largest number of infected people
per 100,000 population, 208 people, followed by
148 in Liberia and 32 in Guinea. Figures 1 and 2
provide the trends and the total number of cases
and fatalities in the three countries.
Those most vulnerable to EVD include: people
in the border regions where infections can be
transmitted easily through porous borders;
people in capital cities where infected people
may go for treatment, particularly those in the
slums of these cities; and poor rural areas with
inadequate clean water and sanitation facilities,
and where the tradition for caring for the sick and
8
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
burial rights are well entrenched. Access to health
services is limited in remote rural and slum urban
areas in these countries, which has hindered the
containment of the outbreak.
The contiguity of the three most affected
countries and loose migratory patterns make
containment challenging and further fuels the
pace of transmission. Ebola is spreading at an
unprecedented rate in the affected countries. In
Liberia, reported cases are doubling every 15-20
days, and in Sierra Leone, every 30-40 days
(Meltzer et al., 2014). The stability in one country
attracts patients looking for treatment from
another. Guinea experienced some relatively high
infected cases between May and June 2014. As
some glimmer of hope appeared in July in Guinea,
Liberia’s crisis peaked between July and October.
As respite began to emerge in Liberia, Sierra Leone
witnessed a very protracted crisis (September 2014
to January 2015), while another phase of intense
crisis re-emerged in Guinea (October 2014 to
January 2015). (See Figure 3 for the dynamics.)
Of the 367 new cases recorded over the past
21 days (as of 11 February 2015), 60.2 percent
(221 cases) were from Sierra Leone and 36.5
percent (134 cases) from Guinea (figure 4). There
is an improved control of the disease in Liberia,
and it is hoped that the crisis will end due to the
relatively high number of new cases in Sierra
Leone and Guinea.
Figure 1: Ebola virus disease epidemic – No. of cases and deaths
Proportion of fatal cases
No. of cases Guinea
No. of cases Liberia
No. of cases Sierra Leone
12,000
0.8
9,000
0.6
6,000
0.4
3,000
0.2
0
APR
2014
MAY
2014
JUN
2014
JUL
2014
AUG
2014
Source: Authors’ computation and compilation from Github (2015) and WHO (2015).
147
Center
SEP
2014
Right
OCT
2014
NOV
2014
DEC
2014
JAN
2014
0.0
left
Figure 2: Evolution of the epidemic in Guinea, Liberia and Sierra Leone
Guinea
Liberia
Sierra Leone
All countries
25,000
20,081
6,553
2,500
2,599
7,842
3,083
1,422
250
Cases
Deaths
Cases
20 August 2014
Deaths
Cases
1 October 2014
Deaths
28 December 2014
Source: Authors’ compilation from WHO data.
147
Center
Right
left
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
9
Figure 3: The Patients Database – Confirmed weekly EVD cases reported nationally, 24 February 2014 to
1 February 2015
Guinea
Liberia
Sierra Leone
600
450
300
0
3/3-3/9
3/10-3/16
3/17-3/23
3/24-3/30
3/31-4/6
4/7-4/13
4/14-4/20
4/21-4/27
4/28-5/4
5/5-5/11
5/12-5/18
5/19 -5/25
5/26 -6/1
6/2-6/8
6/9-6/15
6/16-6/22
6/23-6/29
6/30-7/6
7/7-7/13
7/14-7/20
7/21-7/27
7/28-8/3
8/4-8/10
8/11-8/17
8/18-8/24
8/25-8/31
9/1-9/7
9/8-9/14
9/15-9/21
9/22-9/28
9/29-10/5
10/6-10/12
10/13-10/19
10/20-10/26
10/27-11/2
11/3-11/9
11/10-11/16
11/17-11/23
11/24-11/30
12/1-12/7
12/8-12/14
12/15-12/21
12/22-12/28
12/29-1/4
1/5-1/11
1/12-1/18
1/19-1/25
1/26-2/1
150
Source: Authors’ compilation from WHO data.
Figure 4: New cases over the past 21 days, as of 11 February 2015
147
Center
Right
left
221
134
12
Liberia
Guinea
Sierra Leone
Source: UNDP-RBA (2015b).
As indicated in figure 1, the containment
of
147
EVD is yet to be achieved. However, efforts of
the different governments and the international
community, non-governmental organizations
(NGOs) and local communities have started
yielding results by helping to reduce the fatality
rate, which decreased from around 70 percent in
May 2014 to 54.7 percent in August, and to 39.1
10
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
percent in December 2014. Figure 1 also shows
the cumulative and the dynamics of the fatality
rate for the three countries. Guinea registered
the highest mortality rates, almost stabilizing
between August and December 2014; from 66.9
percent in August, it declined by only 4 percentage
points to 62.9 percent in December. By contrast,
efforts seem to be more effective in the other
two countries; the mortality rate decreased from
57.67 to 42.79 percent in Sierra Leone. Figure 5
provides the actual cases and fatality rates in each
of the three epicentre countries. Guinea has the
highest fatality rate in spite of having the lowest
number of infection cases, which suggests a very
weak treatment capacity compared to that Liberia
and Sierra Leone.
Ebola patients. The level of infection ranges from 166
health workers in Guinea to 371 in Liberia. Sierra
Leone, however, presents the highest level of fatality
among health workers (figure 6). This current EVD,
therefore, has the highest infection cases and deaths
among health professionals, which has further
depleted inadequate trained health personnel in
these epicentre countries. The high incidence on
health workers has led people to believe that the
health centres are the main source of infection. This
accounted for the low patronage of health services
for both Ebola and non-Ebola-related illnesses.
One of the complexities created by EVD in West
Africa is the high level of infection and deaths among
healthcare workers, particularly those working with
Figure 5: Ebola virus disease: Number of cases, deaths and fatality rates, as of 7 January 2015
Deaths
No of inffected cases
Fatality rate = 64.1%
Fatality rate = 36.1%
Fatality rate = 35.3%
Fatality rate = 45.3%
20,712
9,780
8,157
1,781
2,775
3,496
2,943
Guinea
8,220
Liberia
Sierra Leone
Total
Source: UNDP-RBA (2015b).
Figure 6: No. of cases of Ebola virus disease, deaths and fatality rates among health workers,
as of 11 February 2015
Guinea
Liberia
Sierra Leone
147 371 Center
Right
left
293
221
179
166
88
53.01
Cases of infection
75.42
48.25
Deaths
Fatality rate
Source: Authors’ computation from the WHO database.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
11
EVD does not respect age; all age groups
are affected. The most active segment of the
population (15-44 years), the labour force, is
heavily affected, accounting for around 57 percent
of total infections. This is followed by those in the
age group of 45 years and above (23%). Children
are also not spared (figure 7). The people who
contribute most to national productivity have
been also those mostly affected by EVD. This may
explain why the impact of EVD on economic
activity, poverty and food security could be
very high. (See figure 7 for the demographic
breakdown of the infected cases in Guinea, Liberia
and Sierra Leone.) In terms of numbers affected
per 100,000 population, those in the age group of
45 years and above are mostly affected, followed by
the age group of 15-44 years (table 1).
In addition to these three countries, three other
countries have officially reported cases of Ebola
in their territory: Senegal, with an imported case
from Guinea; Nigeria, which recorded 20 cases
including eight deaths; and Mali with eight cases,
including six deaths (figure 8). The three countries
have been declared free of Ebola as a result of the
proactive measures for containing the EVD, such
as the use of volunteers trained in epidemiological
issues (Mali), and the involvement of the private
sector and the decentralization of the health
management system (Nigeria and Senegal).4
Figure 7: Demographics of those affected by the Ebola virus disease, as of 11 February 2015
Guinea
Liberia
Sierra Leone
311
222
214
175
55
10
0-14 years
208
148
91
51
35
15-44 years
32
45 years
and above
Average for all
age groups
Source: Authors’ computation from WHO Database on Ebola.
Table 1: Total number of confirmed and probable cases, by gender and age group, in Guinea,
Liberia and Sierra Leone
Total number of cases
By group*
(per 100,000 population)
Country
147
Male
By age group†
(per 100,000 population)
Center
Female Right 0-14left
years
12
45+ years
Guinea
1,413 (26)
1,508 (28)
460 (10)
1,648 (35)
791 (51)
Liberia
2,801 (141)
2,746 (140)
943 (55)
2,981 (175)
1,145 (214)
Sierra Leone
5,037 (177)
5,400 (186)
2,201 (91)
5,751 (222)
2,298 (311)
Source: WHO (2015).
Note: * Excludes cases for which data on gender are not available. † Excludes cases for which data on age are not available.
4
15-44 years
The three countries were declared Ebola-free: Senegal (17 October 2014), Nigeria (19 October 2014) and Mali (18 January 2015).
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Figure 8: Mapping of the Ebola virus disease in Africa
Source: WHO, UN; The Economist. www.Economist.com/graphicdetail
Note: * Declared Ebola-free. † Excluding Congo
As at October 2014, although no secondary
EVD cases had occurred outside Africa, a new
dimension was introduced to the evolution of
EVD – its globalization. Health workers in Spain
and the United States contracted EVD while
providing care for Ebola patients. This further
reinforces the global threat poses by this pandemic
and the urgent need for global action against the
virulent disease.
In contrast to the past outbreaks in Sudan, Gabon,
DRC and Uganda, which were localized and
mostly restricted to remote areas where they were
detected, the West African case is geographically
widespread. It spread from remote places such as
Macenta and Guékédou to Conakry in Guinea,
and from Kailahun and Bombali to Freetown in
Sierra Leone, as well as from Lofa and Nimbia
to Monrovia in Liberia. The age-long communal
ties, the fluid cross-border movement and cultural
practices facilitated this geographical spread.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
13
Figure 9: Geographical spread of the Ebola virus disease, as of 11 February 2015
Source: WHO (2015).
14
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
The Ebola epidemic is virulent, particularly in
West Africa. Lewnard et al. (2014) estimated
a basic reproductive rate (i.e. the average
number of secondary infections produced by a
primary infection) for EVD at 2.5, compared to
1.8 for Congo in 1995 and 1.3 for Uganda in 2000
(Chowel, 2004).5,6
Why is the basic reproductive rate highest in West
Africa? Several factors drive the spread of EVD
in the region. First, health professionals often
misdiagnosed the EVD since its early symptoms
resemble those of other diseases endemic to the
region (e.g. malaria, cholera and Lassa fever).
EVD presents many similar symptoms to other
viral infections, with non-specific signs such as
fever, asthenia and body aches. After a few days,
the predominant clinical syndrome becomes a
severe gastrointestinal illness with vomiting and
diarrhea. These symptoms can also result from
a number of other diseases that are prevalent
in the region, thus contributing to the silent
spread of the virus, which remains hidden and
eludes containment measures. The first case was
recorded on 26 December 2013, but the virus was
not officially declared as Ebola until 21 March
2014 (WHO, 2015a).
Fear spreads as fast and as wide as a virus. The
high mortality rate associated with Ebola threatens
the ability to perform many interventions that
could help contain the epidemic. Indeed, due
to fear of infection, the public is reluctant to
engage in contact tracing; infected persons are
hesitant to present for treatment; and clinicians
are frightened to provide care.7 In this context,
medical staff have felt a certain unease about
treating a highly transmissible infection for
which there is no vaccine, no specific therapy,
and a high mortality rate. As a result, a paucity
of knowledge on the disease, combined with the
fear produced by the epidemic, may have delayed
the implementation of simple interventions
to prevent deaths (Lamontagne et al., 2014).
High mortality rates, in turn, have fuelled fears
surrounding the disease among both medical
staff and the population at large. This awareness
of the existence and magnitude of the epidemic
came months too late and the virus had spread
considerably.
A humanitarian crisis loomed, economic activities
and livelihoods were destroyed, communal
ties and trust were weakened, and security was
threated. What appeared as a public health crisis
had transformed into a development crisis of
significant proportion. It is imperative that this
vicious and virulent virus be stopped, not only
because the human toll is already very heavy, but
also due to the medium- to long-term consequences
for human development in the region, which are
extremely worrisome. Moreover, some observers
consider that, in addition to the deaths directly
attributable to Ebola, many more deaths will have
resulted from indirect repercussions, such as the
failure to treat patients who do not have Ebola, but
rather other diseases with similar symptoms.
All three countries have recently emerged
from civil conflicts or political instability that
resulted in countless deaths, economic crises,
and a severe deterioration in social conditions.
TPrevious EVD outbreaks were reported in Zaire in 1976 (first outbreaks), Sudan (1979, 2004), Côte d’Ivoire (1994), Gabon (1994, 1996, 1997 and 2001–2002), Republic of the Congo
(2001–2002, 2003 and 2005), DRC (1995, 2007–2008, 2008– 2009) and Uganda (2000, 2007, 2011).
6
Fisman (2014) estimates a weaker basic reproductive number of 1.6 to 2.0.
7
Between 20 October and 9 November, 72.0 percent of all reported patients with EVD were isolated in Guinea, compared to 20.0 percent in Liberia and 13.0 percent in Sierra Leone
(UNDP, 2014a).
5
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
15
The re-establishment of peace in the context
of democratically elected governments has
launched economic recoveries that have been
accompanied, to varying extents, by improved
social indicators. This is highlighted by evidence
from the three national studies from Guinea,
Liberia and Sierra Leone (UNDP, 2014 a-b;
UN, 2014):
• Since 2011, Guinea has been emerging from a
profound political, economic and social crisis.
The deterioration of democratic institutions
and the social fabric, compounded by poor
management of resources and unmet social
expectations of the population, had serious
consequences for peace and internal stability.
These forces were accentuated by the impact
of the civil wars in Liberia and Sierra Leone,
with disastrous consequences for Guinée
Forestière, which borders both countries. The
crisis was reflected in a decline in per capita
income of 0.6 percent per year from 2000 to
2010, and a rise in the share of the population
in poverty from 49 percent in 2002 to 58
percent in 2010 (UN, 2014).
• After 14 years of civil conflict, Liberia was in
shambles. Health facilities were destroyed, food
insecurity was rampant, poverty rates were high,
and huge numbers of people were displaced.
The democratically elected government since
2006 has sought to re-establish stability in
the context of rapid development and access
to humanitarian assistance (UNDP, 2014a).
Despite various programmes undertaken
to address Liberia’s severe developmental
challenges, most families cannot afford a single
meal a day or fees for basic health services.
Liberia remains a very poor country.
16
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
• Sierra Leone suffered a devastating civil war
from 1991 to 2002, which left 50,000 dead, an
average income of around 38 cents a day, and 2
million displaced persons, or almost a third of the
total population. The health system was almost
completely destroyed. The restoration of peace
and democratic rule since 2002 encouraged very
rapid growth. GDP rose by 9.5 percent per year
in real terms from 2002-2013, and per capita
income in current dollars quintupled. The share
of the population living on less than a dollar a
day declined only marginally, however, from
59 percent in 2003 to 57 percent in 2012 (UNDP,
2014b). A large proportion of Sierra Leoneans
are still too poor to afford basic necessities of life
including health services.
The health systems in Guinea, Liberia and
Sierra Leone were unprepared for Ebola at the
outset. They lacked sufficient amounts of all
that is required to contain the epidemic: drugs,
ambulances, facilities, trained health personnel,
and many other items. This is not surprising since
these countries had few resources and suffered
from many serious health issues that generated
competing demands for resources, even prior
to the onset of Ebola. Tragically, the shortage
of protective equipment resulted in multiple
infections and deaths among medical personnel
and further spread the disease. Also, rumours
led people to avoid treatment for fear of being
infected. Table 2 provides an overview of how the
deficiencies in the health system hindered EVD
containment in West Africa, focusing on human
resources, governance and leadership, funding,
commodities and supply chain networkers, service
delivery and information.
Moreover, impoverished rural areas have more
limited access to services than relatively well-off
urban areas. This inequitable distribution of human
and financial resources has hampered the response
to the epidemic, which originated in, and continues
to heavily affect, many rural areas. Human
resources are inequitably distributed. Conakry,
which is home to just 15 percent of the population,
has 75 percent of the health workers. By contrast,
Guinée Forestière, which has been hardest hit by
the Ebola epidemic and is home to 22 percent of the
Guinean population, has 9 percent of healthcare
workers (UNDP-RBA, 2014a). This shows that the
affected countries’ health systems were not ready
for the outbreak because they were not equipped
to cope with the epidemic. Indeed, before the EVD
epidemic, Liberia had only 2.8 healthcare workers
per 10,000 people and 51 medical doctors serving
its population of 4.29 million (UNDP, 2014a;
Lewnard et al., 2014). The situation is the same in
Sierra Leone and very similar in Guinea. Table 3
shows the poor state of the health systems in these
countries with respect to inputs and outcomes in
terms of number of physicians per 1,000 people,
hospital beds, mortality rates, risk of maternal
deaths, access to water, etc. Table 4 also shows that
most countries in the region are not better-off than
the three epicentre countries. For instance, only
Cape Verde and Nigeria have a greater number
of physicians per 1,000 population. Most other
countries are in the same health sector conditions
as those in Guinea, Liberia and Sierra Leone. They
are not prepared for any serious public health crisis
such as the EVD outbreak.
Table 2: Impact of health system deficiencies on Ebola outbreak containment
Governance
Financing
Human
Resources
Description
Policies, strategies,
and plans that
inform the
course of action
that a country
will take to meet
the health needs
of its people.
Mechanisms used
to fund health
efforts and
allocate resources.
The people who
provide health
care and support
health delivery.
Goods that are
used to provide
healthcare.
The management The collection,
and delivery of
analysis, and
healthcare.
dissemination of
health statistics
for planning and
allocating health
resources.
Impact of
Health System
Component
Deficiency in
Ebola Context
Slow initial
government
response to the
Ebola outbreak
and incapacity
to implement
national Ebola
plans has
diminished
public confidence
in political
authorities and
limited efforts to
dispel rumors and
fears about Ebola.
Insufficient
financial
resources
to fund local
responses and
pay health
personnel
contribute to
human resource
and commodity
shortages.
Shortages of
not only health
personnel, but
also support staff
such as grave
diggers and
statisticians
limit the ability
to detect,
prevent, and
treat EVD cases.
Insufficient
supply of
protective
equipment
threatens the
safety of
healthcare
workers
(including
community
volunteers) and
is associated with
hospital- and
clinic-based
infections.
Many health
facilities in
Liberia and
Sierra Leone
remain closed
due to staff
shortages and
other factors.
Commodities
Service
Delivery
Information
Limited capacity
to conduct
contact tracing
and diagnosis
calls into
question the
actual EVD cases
and impedes
efforts to detect,
treat, and control
the virus.
Source: Salaam-Blyther (2014).
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
17
Table 3: Infrastructures and development indicators in the epicentre countries
Guinea
Indicators
Year
Value
Sierra Leone
Year
Value
Liberia
Year
Value
Literacy rate, youth total (% of people ages 15-24)
2010
31.4
2012
62.7
2007
49.1
Literacy rate, adult total (% of people ages 15 and above)
2010
25.3
2012
44.5
2007
42.9
School enrolment, primary (% net)
2012
74.4
–
–
2011
40.6
School enrolment, secondary (% net)
2011
30.4
–
–
–
–
Prevalence of anaemia among children (% of children under 5)
2011
75.7
2011
74.1
2011
71.5
Prevalence of HIV, total (% of population ages 15-49)
2013
1.7
2013
1.6
2013
1.1
Mortality rate, neonatal (per 1,000 live births)
2013
32.8
2013
44.3
2013
25.6
Mortality rate, under-five (per 1,000 live births)
2013
100.7
2013
160.6
2013
71.1
Immunization, DPT (% of children ages 12-23 months)
2013
63.0
2013
92.0
2013
89.0
Immunization, measles (% of children ages 12-23 months)
2013
62.0
2013
83.0
2013
74.0
Hospital beds (per 1,000 people)
2011
0.3
2006
0.4
2010
0.8
Physicians (per 1,000 people)
2010
0.1
2010
0.0
2010
0.0
Lifetime risk of maternal death (%)
2013
3.3
2013
4.7
2013
3.2
Improved sanitation facilities (% of population with access)
2012
18.9
2012
13.0
2012
16.8
Newborns protected against tetanus (%)
2013
80.0
2013
87.0
2013
91.0
Prevalence of undernourishment (% of population)
2012
15.2
2012
29.4
2012
28.6
Life expectancy at birth, female (years)
2012
56.6
2012
45.5
2012
61.2
Life expectancy at birth, male (years)
2012
55.1
2012
45.2
2012
59.3
Life expectancy at birth, total (years)
2012
55.8
2012
45.3
2012
60.2
Health expenditure per capita (current US$)
2012
32.0
2012
95.7
2012
65.5
Health expenditure, public (% of GDP)
2012
1.8
2012
2.5
2012
4.6
Improved water source (% of population with access)
2012
74.8
2012
60.1
2012
74.6
Sources: World Development Indicators Database 2014.
Among the greatest impediments to controlling
the disease were the real disadvantages that
underpin the countries’ development contexts –
a combination of fear, distrust and ignorance.
Fear of being quarantined or being infected at
health centres has discouraged both testing and
treatment. Widespread stigmatization of persons
who are infected with, or have survived, the
disease has also limited willingness to be tested
and treated. Relatives have been unwilling to
bring bodies for safe disposal because standard
protection against the spread of the infection
18
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
involves the burning of bedding, mattresses and
clothes of the person infected. Communities have
been unwilling to cooperate with medical teams or
with those responsible for monitoring contacts. At
the extreme, health workers and people involved
in tracing contacts have been threatened or
physically assaulted (WHO, 2015a), requiring the
use of security personnel for protection.
Control efforts in the epicentre countries have been
hindered by community resistance. Several factors
account for this. First, fear and misconception
about the unfamiliar disease play an important
role. Second, the inability of ambulances and burial
teams to respond quickly provoked confrontation
from the affected communities. Third, little or no
information about patients’ conditions, outcomes
of their treatment and places of burial also created
misgivings. Fourth, ignorance fuels resistance.
The perception that spraying chlorine was actually
spreading the disease created fear that degenerated
into physical assault of EVD workers. And finally,
anger was sparked among caregivers who had
not been for weeks or months. Sometimes, they
were asked to work under unsafe conditions, in
contrast to their right to work under equitable and
satisfactory environments while receiving equal
pay for equal work.
Benin
Burkina
Faso
Cape
Verde
Côte
d'Ivoire
The
Gambia
Ghana
GuineaBissau
Mali
Niger
Nigeria
Senegal
Togo
Table 4: Infrastructures and development indicators in non-epicentre West African countries
Literacy rate, youth total (% of people ages 15-24)
42.4
39.3
98.1
48.3
69.4
85.7
74.3
47.1
23.5
66.4
66.0
79.9
Literacy rate, adult total (% of people ages 15 and above)
28.7
28.7
85.3
41.0
52.0
71.5
56.7
33.6
15.5
51.1
52.1
60.4
School enrolment, primary (% net)
94.9
66.4
97.2
61.9
70.9
87.1
69.8
68.7
62.8
63.9
73.3
90.4
School enrolment, secondary (% net)
–
19.7
69.1
–
–
51.5
–
34.5
12.2
–
–
–
Prevalence of anemia among children (% of children
under 5)
65.4
86.1
60.5
74.5
65.4
76.1
71.3
80.1
75.6
71.0
20.8
71.1
Prevalence of HIV, total (% of population ages 15-49)
1.1
0.9
0.5
2.7
3.2
1.3
3.7
0.9
0.4
3.2
0.5
2.3
Mortality rate, neonatal (per 1,000 live births)
26.9
26.9
11.4
37.5
37.4
29.3
44.0
40.2
27.5
37.4
23.0
30.4
Mortality rate, under-5 (per 1,000 live births)
85.3
97.6
26.0
100.0 117.4 78.4
123.9 122.7 104.2 117.4 55.3
84.7
Immunization, DPT (% of children ages 12-23 months)
69.0
88.0
93.0
88.0
58.0
90.0
80.0
74.0
70.0
58.0
92.0
84.0
Immunization, measles (% of children ages
12-23 months)
63.0
82.0
91.0
74.0
59.0
89.0
69.0
72.0
67.0
59.0
84.0
72.0
Hospital beds (per 1,000 people)
0.5
0.4
2.1
0.4
1.1
0.9
1.0
0.1
0.3
0.5
0.3
0.7
Physicians (per 1,000 people)
0.1
0.0
0.3
0.1
0.0
0.1
0.0
0.1
0.0
0.4
0.1
0.1
Lifetime risk of maternal death (%)
1.7
2.3
0.1
3.4
3.2
1.5
2.8
3.9
5.0
3.2
1.7
2.2
Improved sanitation facilities (% of population
with access)
14.3
18.6
64.9
21.9
27.8
14.4
19.7
21.9
9.0
27.8
51.9
11.3
Newborns protected against tetanus (%)
93.0
88.0
92.0
82.0
60.0
88.0
80.0
85.0
81.0
60.0
91.0
77.0
Prevalence of undernourishment (% of population)
6.1
25.0
9.6
20.5
7.3
5.0
10.1
7.3
13.9
7.3
21.6
15.5
Life expectancy at birth, female (years)
60.5
56.5
78.5
51.2
52.4
61.9
55.6
54.5
58.1
52.4
64.7
57.0
Life expectancy at birth, male (years)
57.8
55.3
70.8
49.6
51.8
60.0
52.5
54.7
57.8
51.8
61.8
55.3
Life expectancy at birth, total (years)
59.1
55.9
74.5
50.4
52.1
60.9
54.0
54.6
58.0
52.1
63.2
56.2
Health expenditure per capita (current US$)
33.1
37.8
144.2 87.9
94.3
83.0
29.8
42.1
25.5
94.3
51.2
40.8
Health expenditure, public (% of GDP)
2.3
3.4
3.0
1.9
1.9
3.0
1.3
2.3
2.8
1.9
2.8
4.4
Improved water source (% of population with access)
76.1
81.7
89.3
80.2
64.0
87.2
73.6
67.2
52.3
64.0
74.1
60.0
Sources: World Development Indicators Database 2014.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
19
The long-standing cultural practices that people
were understandably reluctant to abandon
contributed to further spreading the infection.
Due to the culture of burying the dead near
their ancestors, corpses had to be moved long
distances, therefore contributing to a resurgence.
Caring for the sick by friends and relatives, who
are predominantly women, is an important duty
in these societies, but the provision of care by
untrained and unequipped people accounts for
the further spread of EVD. Moreover, the washing
and dressing of the deceased is a show of respect
in some of the cultures in the area, but again,
further transmitted the disease. For this reason,
the cremation of corpses of the victims was heavily
resisted in these countries. Funeral and burial
practices in these countries are exceptionally high
risks. Evidence from Guinea shows that around 60
percent of the infections were caused by burial and
funeral practices (WHO, 2015a).
Evidence from the three epicentre countries also
underscores the human rights dimension of EVD
as an important source of anxiety and fear that
renders people more vulnerable to the disease.
The social, cultural, economic and other rights of
the affected populations have been violated since
the outbreak. The lack of respect for the right to
information, participation and education, which
created a culture of fear and mistrust, complicated
the containment process and its effectiveness. Some
of the measures taken by affected governments to
stop the spread of the disease inadvertently and
negatively affected the human rights to freedom
of movement and assembly, the right to culture,
and the freedom of religion. This is, to a large
extent, one of the sources of strong resistance that
made containment difficult in many communities.
Even at the community level, the stigmatization of
survivors of the EVD violates their right to equality
and non-discrimination (OHCHR, 2015).
20
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
The wide geographical dispersion of the EVD
overwhelmed the capacity of the health personnel.
This sometimes caused exhaustion among
health workers, another source of exposure. The
high number of infections and fatalities among
health personnel encouraged the perception that
“hospitals were places of contagion and death”. In
Guinea, for instance, 90 of the 153 infected health
workers died, and in Liberia 79 of 152 died. Most
of infections between October and December
in Guinea, for instance, occurred in non-EVD
centres. As a result, the patronage of traditional
healers and self-medication became the preferred
options in these countries.
The difficulty of coordinating aid (medical,
food-related, etc.) and of treating infected
patients using existing infrastructures is another
impediment. Instead of working with national
authorities to ensure effective coordination of the
response action, many development partners and
donors engaged in duplicative and competitive
activities, including the establishment of the
cluster systems in Liberia and Sierra Leone. This,
to some extent, weakened government leadership.
This poses some questions. How, in the context of
an acute health crisis, can government authorities
be enabled to assert their leadership? What role
should UN agencies play in order to allow national
governments to express greater leadership in
managing the crisis? Should UN agencies rethink
their role in the context of a crisis that widely
exceeds a national context? Is it conceivable and
desirable for regional and sub-regional authorities
to assume greater responsibility in carrying out
related actions? Indeed, how can other countries
in the region, which are not directly affected
by the epidemic but face its looming risks and
consequences, contribute to solving the problem?
What would their mandate be? The answers to these
questions would ensure effective coordination.
The United Nations aims to address these issues
through the formation of the UN Mission
for Ebola Emergency Response (UNMEER)
and the recent directive of the UN SecretaryGeneral on coordination of the early recovery
response actions.
The fact that the average number of daily cases is
falling in countries such as Guinea and Liberia
does not imply that the international community
should relax. It fell in Guinea between June and
August but became intense between September
2014 and January 2015. Any unattended new
case could spur a localized epidemic. Enhanced
awareness campaigns are still needed in all
countries. Stigmatization is still an issue. Findings
from the recent exploratory mission of Médecins
Sans Frontières (MSF, Doctors Without Borders)
to Bong county (Liberia) prove illuminating: “MSF
found that people who had been in contact with
the sick were fleeing into the bush so as not to be
traced as a contact or taken to a case management
facility, fearful of what may happen” (MSF, 2014b).
Addressing the key factors propagating the spread
of this disease should be given priority attention;
however, it is important to note that there is no
panacea. A combined strategy of intensifying
contact tracing to remove infected individuals
from the general population and placing them
in a setting that can provide both isolation and
dedicated care has proved effective. It requires that
holding or treatment centres have the necessary
supplies, basic facilities and trained personnel.
The successful containment in DRC, Mali, Nigeria
and Senegal is positive, showing that the disease
can be stopped if a country is adequately prepared
from the outset. The fact that a densely populated
country such as Nigeria could bring EVD under
control in a very short time offers encouragement
to other developing countries. Some innovations
in these countries offer some good lessons to
other countries. In these countries, the high-level
of alert is a major feature of the response action.
Providing means of transportation such as
motorcycles, canoes, communications equipment
such as satellite phones for EVD investigations
and contact tracing, as well as leadership that
demonstrates a high level of concern for the affected
population have contributed significantly to the
success in containing the disease in the DRC. The
use of medical students trained in epidemiology
to rapid increase the number of contact tracers
is one of the drivers of success in Mali. Unique
lessons learned can be drawn from Nigerian
and Senegal experiences. Collaboration among
stakeholders, coordination between government
and
development
partners,
high-quality
laboratories, the setting up of separate emergency
centres outside the health facilities, massive public
information campaigns and effective contract
tracing made it possible for Nigeria and Senegal
to tame EVD. (See box 1 for key drivers of success
in Nigeria.)
Key lessons drawn from the field underscore the
importance of:
• dealing with the psychological factors that must
be combated through better communication
and improved trust between populations and
care organizations;
• improving coordination between government
and non-governmental organizations (NGOs),
development partners and the private sector;
• improving the quantity, coverage and quality of
the health systems;
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
21
• learning from the experiences of countries that
succeeded in containing the epidemic. The
rapid responses from Senegal and Nigeria are
positive lessons learned. In these countries,
there were competent and relatively adequate
health personnel, decentralized health systems,
community engagement and strong leadership
commitment. Box 1 presents some of the
success factors for Nigeria’s containment of
the EVD;
• developing a long-term plan of how to address
some of the behavioural and cultural issues that
aided the spread of the disease;
• revelaing that the international community lacks
the capacity to respond to a severe, sustained and
geographically dispersed public health crisis;
• showing
that
community
engagement,
acceptance and ownership of the response
matter;
• revealing that failure to respect peoples’ tradition
is a recipe for failure;
• promoting the strong commitment of political
leaders elicits citizens’ engagement;
• communicating Ebola protocols to the people.
In fact, the West African EVD rekindled
dynamism in the preparation, renewal and
communication of Ebola protocols.
2.2. The Ebola virus disease in
West Africa: Gender and children
dimensions
2.2.1 Gender dimension
Analysis of the gender dimension of the EVD is
not only vital to examining the social aspect of the
outbreak, but it also adds value to recovery efforts
and programmatic interventions. Although not
all officially available epidemiological data are
disaggregated by gender and age in all the
affected countries,8 it is evident that women are
heavily affected by EVD, both directly, by infection,
and indirectly, by the associated social and
economic impact.
The number of EVD cases is higher among
women than men in the three epicentre countries
- 50.8 percent have been women, as of 7 January
2015. On per 100,000 population, women are
more affected – 118 per 100,000 population
against 115 for men. The gender disparity is more
pronounced in Guinea and Sierra Leone; it is
relatively lower in Liberia (figure 10).9 However,
evidence from a UNICEF report, at the early
stage of the outbreak in Liberia, shows that men
account for 25 percent, and women for more
than 50 percent.10 Table 1 also provide gender
dimension of the infection cases.
Guinea provides a good example of the gender
dimension of EVD epidemiology. The epidemic
affects more women (53%) than men (47%), a
disparity that could be explained by the role of
women within the family as the primary carers of the
sick and thus more exposed to infection (UN, 2014).
The situation is even worse at the sub-national level.
In Liberia and Sierra Leone, data collection tools for the gap analysis using a gender lens have been designed and adopted by UN Women and are in use for data collection at the community
and household levels. Evidence from WHO databases has shown some significant improvement in this regard.
9
Disaggregation by gender and age group is challenging. The statistics in figure 10 do not reflect the total gender disaggregation; it only captures where where gender disaggregated information
is known for Liberia, Sierra Leone and Guinea. See apps.who.int/gho/data/view.ebola-sitrep.ebola-summary-age-sex-20150107?lang=en
10
For more information, see www.voanews.com/content/ebola-has-devastating-impact-on-children-in-liberia/2448520.html
8
22
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
For instance, a high percentage of infected people are
women in Gueckédou (62%) and Télémilé (74%). In
Liberia, there are more fatality cases among women
(55.2%) than among men (44.2%). In Sierra Leone,
men and women are almost equally affected; as at
7 October 2014, around 50.6 percent of all confirmed
cases were males and 49.4 percent, females. Figure 10
provides the total number of cases by gender.
Several factors explain the predominance of
women among the victims. The first factor
explains that the gender difference in the death
rate is related to sociological aspects of affected
communities. As care providers, women are more
likely to be exposed to the disease transmission
vectors such as vomit or other bodily fluids of an
infected family member. Furthermore, certain
traditional practices and rituals for honouring
the deceased that women typically perform also
pose an increased risk. The national study from
Liberia on the socio-economic impact of the EVD
highlights the reasons that more women were
infected than men (UNDP 2014a). For instance,
70 percent of the respondents interviewed
said that it was because women were natural
caregivers; it was their role to take care of their
husbands, children and relatives should they fall
ill. This traditional role of caring for the elderly,
children and the sick put women at a direct risk
of contracting the virus. In addition, medical
professionals are highly exposed; on the front lies
of the disease, regardless of gender, many of them
have fallen victim to the EVD. As of 18 September
2014, in Guinea, Sierra Leone and Liberia,
non-disaggregated data records cases of a total of
318 healthcare workers, of whom 144 died, i.e. a
mortality rate of 47 percent. The female exposure
to the disease is intensified by the fact that, in
most of cases, hospital settings in West Africa
involves more female nurses, cleaning ladies and
laundry workers than male orderlies and cleaners.
A female working in a local hospital with Ebola
patients has more frequent contact with patients
and the objects they come in contact with.
In addition, women dominate the trading
activities in most West Africa. Cross-border
trading was a major source of contracting EVD,
which further exposes them.
Figure 10: Cumulative number of cases of the Ebola virus disease in Guinea, Liberia and Sierra Leone, by gender,
as of 7 January 2015
Male
Female
3,891
2,538
4,151
2,444
1,410
1,309
Guinea
Liberia
Sierra Leone
Source: WHO (2015b).
147
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
23
With most medical facilities and personnel devoted
to Ebola, women needing non-EVD medical
attention cannot access health services. Indeed, the
limited health resources with Ebola particularly
affect expecting mothers. The demands caused by
Ebola have left very few resources for pregnant
women, who already face limited access to adequate
healthcare. For example, in Bong, one of the most
populous counties in Liberia, the ambulance for
obstetric emergencies was used for the Ebola
response. In addition, the Surgical and Emergency
Departments at JFK Hospital, one of the country’s
major referral hospitals, closed (UNFPA, 2014). In
2014, approximately 800,000 women in Guinea,
Liberia and Sierra Leone gave birth, of whom
120,000 faced life-threatening complications.
These risks are compounded by the fear of
contracting Ebola at hospitals and treatment
centres, which deters women from seeking care
during delivery. Many women are giving birth at
home rather than at health centres. When Ebola
struck Liberia in August, the proportion of births
supervised by a health professional dropped from
52 to 38 percent. In Sierra Leone, the number
of women giving birth in hospitals and health
clinics dropped by 30 percent. Evidence from
the national assessment study reveals a drastic
reduction in the number of pregnant women
seeking care between May and September 2014:
from 164 to 31 (Bombali districts); from 333 to 26
(Kenema districts); and from 33 to 22 (Koinadugu
district) (UNDP, 2014b). This therefore worsens
the initial condition for rolling out and
implementing the SDGs.
Before the onset of the epidemic, expectant mothers
in Guinea were at high risk of maternal death
(724 maternal deaths per 100,000 live births).
11
12
24
With the epidemic, there has been a breakdown
in health services. Of the approximately 200,000
expected pregnant women in the last quarter
of 2014, nearly 40,000 may not be monitored or
may not have their babies delivered by a qualified
person (UNDP-RBA, 2015b).
The EVD’s knock-on effects are huge. It has caused
many deaths, stifled growth rates, reversed recent
socio-economic gains, aggravated poverty and
food insecurity, and destroyed livelihoods. Hidden
in the aggregated impact is the plight of Ebola’s
voiceless victims and agents of change – women
(UNDP-RBA, 2015b). Beyond being physically
affected by the epidemic, women have suffered
reversals in economic empowerment, because of
reduced economic activity related to EVD control
measures that restrict the movement of people
and goods. In their role as economic providers for
their families, women have experienced sharper
economic impacts than men. Women in the three
countries are disproportionately clustered in the
least productive sectors, with 90 percent employed
in the informal services and agricultural sectors.11
EVD has increased women’s vulnerability a loss
of livelihoods and incomes. For instance, as of
October, in Sierra Leone, 54 percent of smallholder
farmers, who make up one fifth of infections, were
women. The reduction in trade and the closing of
borders as well as in farming activities have had a
negative impact on these women, their incomes
and livelihoods. Furthermore, due to the epidemic,
women’s two main sources of funding have dried
up. For instance, in Guinea, the tontine12 gathering
is no longer held, and microfinance institutions
have substantially reduced loans to the women due
to a lack of clarity about the future. According to a
FAO and WFP reports (2014), the financial capital
See UNDP (2015) on how EVD increased women’s vulnerability to loss livelihoods and incomes in the three countries.
A tontine is an informal savings/credit scheme, known in most Anglophone West Africa as ‘Esusu’. Under this scheme, participants agree to contribute a predetermined amount at a given frequency.
In each round of contribution, one of the participants is designated the recipient of funds from other participants. When each of the contributing participants have received the group fund once, the
cycle of tontine is complete. At the end of a cycle, a new cycle is usually initiated. For the first beneficiary, the tontine is similar to a credit, and for the last participant, the tontine is like a saving.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
After being kept closed for three months due to the Ebola outbreak, schools across Guinea reopened on 19 January 2015. Photo by UNMEER/Martine Perret
of women’s savings and loans groups in Liberia
had been drastically affected since members were
not able to pay back their loans. In addition, rural
women in Melekie, Bong County, who specialized
in poultry farming, could not restock due to border
closure, since Côte d’Ivoire is a major source of
feed and chicks.
In Liberia, as community Ebola task forces were
being established in most communities to monitor
the movements of inhabitants and people from
neighbouring villages, the movement of people
between communities for trade was severely
curtailed. For example, businesswomen were
prevented from going to neighbouring towns and
villages where they normally buy agricultural
products to resell in urban areas (UNDP, 2014a).
Finally, the impact of the epidemic on attendance
at reproductive health services (antenatal clinics,
attended deliveries, caesareans, etc.) is exposing
women to risks associated with pregnancy and
childbirth. They also suffer from the slowdown
observed in the fight against HIV/AIDS. This is the
situation in the three epicentre countries – Guinea,
Liberia and Sierra Leone. There is the likelihood
of increased child pregnancy due to the closure of
schools for more than six months.13
2.2.2 The Ebola virus disease and children
Children are not spared the psychological
trauma created by the EVD. As of January 2015,
there is evidence that 16,600 children had lost
one or both parents or primary caregivers to
Ebola.14 One out of four children affected by the
EVD survived and most children who survived
became orphans.15 Many children in Guinea,
Liberia and Sierra Leone have lost one or both
parents to Ebola since the start of the outbreak
in West Africa. Orphans are usually taken in
by a member of the extended family, but in
some communities, the fear that surrounded
Ebola has become stronger than family ties to
the extent that some of the affected children
feel unwanted and even abandoned. Ensuring
that this group of people is not excluded from
the recovery process is an important challenge
that must be overcome. It also calls for the need
to rekindle the fabric of lives and kinship ties
that were functioning before Ebola in order to
sustain the social capital of extended families
and support to relatives and neighbours. This
extraordinary resilience of communities at a
time of great hardship must persevere.
The feedback from UN Women from Sierra Leone and Liberia tends to support an increase in child pregnancy.
For more information on this and the implications on Guinea, Liberia and Sierra Leone, see www.unicef.org/emergencies/ebola/75941_76129.html. WHO (2015a), however, reports that,
as at December 2014, there were about 30,000 orphans in the three epicentre countries.
15
The story of Moses, a child-survivor from Liberia, reported by Save the Children, provides a vivid illustration of how children were affected by the EVD.
www.savethechildren.org/site/c.8rKLIXMGIpI4E/b.9208417/k.32AA/Left_Behind_by_Ebola_Moses_Story.htm?msource=wexgpebo0914&gclid=CJeZuMaJ3MMCFUkR7Aodl3sA0Q.
13
14
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
25
The EVD is reversing the gains made on the
MDGs in the epicentre countries. Liberia provides
a vivid example of this. When Liberia emerged
from decades of civil war in 2003, the under-five
child mortality rate stood at 110 per 1,000 live
births. Due to efforts of the Government and
its partners, it fell to 75 per 1,000 live births by
2012. The total disruption of the country’s health
system has made children more vulnerable.
Children are once again dying from measles and
other vaccine-preventable diseases. In addition
to the endemic disease in the region that kills
children, about 20 percent of the total EVD
classified into age groups are children (figure 10).
Sierra Leone had the highest number of children
EVD cases (21.4 percent), followed by Liberia
(18.6 percent) and Guinea (15.9 percent).
The closure of schools is a great loss to children in
terms of cognitive learning. All the schools in the
three epicentre countries have been closed since
June 2014. Guinea re-opened all its schools on
19 January 2015, while Liberia reopened its primary
and secondary schools on 16 February 2015 and
tertiary institutions on 4 March 2014. In Sierra
Leone, all schools will be reopened in March 2015.
The total number of learning hours lost to school
closures range from 486 in Guinea to 780 in Sierra
Leone (figure 11). The closure of schools might
also have exposed children to several types of child
abuse (including sexual exploitation and violence
against young girls) with a long-term impact. The
re-opening of schools should be complemented
with back-to-school programmes that focus on
teacher training on school safety, hygiene education
and school sanitation as well as psychosocial care.
Figure 11: No. of learning hours lost due to school closures due to the Ebola virus disease
1,848
780
486
Guinea
582
Liberia
Source: Authors’ compilation.
147
26
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Sierra Leone
Total
2.3 Regional and continental
responses
The containment of the epidemic is beyond the
capacity of a single country. Complementary actions
from sub-regional, regional, continental and global
bodies are important for maximum success. It is
in recognition of this that the Mano River Union
(MRU) and ECOWAS initiated collaborative and
solidarity approaches to dealing with the outbreak.
The cross-border meetings of Ministers of Health of
the MRU (20 June 2014, in Conakry), the Ministers
of Health of West Africa on the Ebola epidemic
(2-3 July 2014, in Accra) and the Heads of State
and Government (HoS&G) of the MRU (1 August
2014, in Conakry) mobilized political leadership
and partnerships to fight the disease. These
meetings underpinned the insufficient capacity
of each country to stop Ebola and highlighted the
urgent need for regional efforts. It also provided
some political backing to cross-border treatment,
testing and contact tracing, and joint response
actions. At the meeting, it was collectively dediced
that it was imperative to scale up resources to
protect health workers and to ensure security
of national and international Ebola workers. It
was also acknowledged that the pandemic was
not only a threat to national security, but also an
impediment to sub-regional, regional and global
security. Therefore, the international community
was called on for support to capacity building for
surveillance, contact tracing, case management
and laboratory testing. Sharing of information,
expertise and resources among Member States was
also emphasized.16 The MRU stakeholders’ meeting
of 4 February 2015, focusing on cross-border
vigilance, and attended by traditional rulers,
communicators, women and youth from the border
communities, provided an opportunity to share
experiences and ideas on how to end the deadly
disease and support recovery efforts.
In addition to donating US$1.0 million to each
of the three most affected countries through
the multi-sectoral coordinated response, the
ECOWAS Authority also established a Regional
Solidarity Fund to fight Ebola, which, as of
6 November 2014, had US$9.2 million as
contributions and pledges.17 It also reaffirmed
the commitment to the Abuja Declaration,
which requires the allocation of 15 percent
of the total budget to the health sector, and
directed its Commission to initiate the creation
of the Regional Centre for Disease Prevention
and Control for West Africa.18 The ECOWAS
Commission, through the West African Health
Organization (WAHO), trained and deployed
around 114 volunteer medical personnel – 49 to
Guinea, on 3 December 2014; 27 to Sierra Leone
on 4 December 2014; and 39 to Liberia on
5 December 2014 in the first instance. ECOWAS
provided US$400,000 to Guinea, Liberia and
Sierra Leone to strengthen their epidemiological
surveillance and response capacity. ECOWAS
HoS&G enjoined Member States to provide
military personnel and logistics to enhance
response capacities, support the medical staff
on the field and participate in the construction
of additional treatment and isolation centres as
well as ensure their security.
For more information on the Declaration of the Mano River Union HoS&G on the eradication of Ebola in the region and related issues, see www.emansion.gov.lr/doc/MRU_EBOLA_jOINT.pdf
This contribution is made up of the following. In addition to the earlier contribution of US$3.5 million by Nigeria to the fund, the country also pledged an additional US$1 million and made
500 health volunteers available to fight the disease. Côte d’Ivoire and Senegal also contributed US$1 million each, while the West African Economic Monetary Union (UEMOA) contributed
US$1.5 million. Countries that have also made pledges include: Benin (US$400,000), Sierra Leone (US$250,000), Niger and Mali (US$200,000 each), and Burkina Faso (US$150,000).
See www./reliefweb.int/report/liberia/ebola-regional-solidarity-fund-boosted-ecowas-member-states-contributions
18
For more information on the main elements of the final communique of the Extraordinary Session of the Authority of ECOWAS Heads of State and Government,
see www.wahooas.org/spip.php?article786&lang=enSee
16
17
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
27
The African Union promoted innovative ideas on
the containment of the epidemic. For instance, on
3 December 2014, the African Union Commission
launched several initiatives aimed at containing
the epidemic (Femmes d’Afrique, 2015). The
mobilization of about 1,000 health personnel
across African countries – the African Union
Support to Ebola Outbreak in West Africa
(ASEOWA). The batch consisted in about 200
volunteers from Nigeria and the second batch,
around 187 medical volunteers from Ethiopia
(a partnership between the African Union and
the governments of Nigeria and Ethiopia). Other
countries that pledged support for the initiative
include Burundi, DRC, Kenya, Rwanda and
Uganda. This initiative aims to bridge the capacity
gaps in the three most affected countries.
The second initiative is the partnership with
the private sector through the mobilization
of telephone operators across the continent
(e.g. Airtel, Etisalat and MTN). It aims at raising
funds for the containment of EVD through sms
campaigns. By sending ‘Stop Ebola’ sms messages
to 7979, US$1.0 is transferred to a dedicated
account that will be used to train, equip and
maintain the volunteers in the affected countries.
This is in addition to US$31.5 million already
contributed to the fight against the disease by the
African private sector operators.
During the January 2015 African Union Summit,
the African leaders resolved to achieve the primary
objective of enhancing disaster preparedness and
response, and reducing avoidable loss of life and
the burden of disease and disability. The African
Centre for Disease Control and Prevention
(ACDCP) was launched with an innovative
mandate to effectively detect and respond to
emergencies, obtain technical support to address
complex health challenges, including surveillance
and research, and build the needed capacity
(African Union and WHO, 2014). The ACDCP
should be empowered to deliver its mandates
effectively and efficiently.
2.4 The international communities’
response to the Ebola
virus disease
The role of the international community has been
helpful. Many bilateral and multilateral agencies
and private sector organizations have responded
to the crisis. Figure 12 highlights financial support
to the UN Ebola Plan as of October 2014. This is
in addition to resources to the three countries and
other organizations working on the pandemic.
The size and length of the outbreak render
estimation of financial needs for the containment
of the pandemic challenging. Based on the
estimated number of cases in early April 2014, the
WHO made an initial appeal of US$4.8 million,
which generated pledges of US$7.0 million from
the international community.19 As a result of the
intensity of the pandemic, the financial estimate
for dealing with the outbreak has been dynamic.
On 1 August, an estimate of US$71.0 million
was announced, which later changed to US$600
in late August, US$1.0 billion on 16 September
and $1.5 billion by mid-November 2014 (Grepin,
2015). By 31 December 2014, the total pledges had
reached US$2.89 billion with only $1.09 billion
actually disbursed or committed – just a little
above one third of the pledges.20 A substantial part
of the resources did not reach the countries until
the end of October – more than six months after
The donations came from the European Commission Humanitarian Aid and Civil Protection Department, Italy, Republic of Korea, the United States of America, Canada, Germany, Japan,
the United Kingdom, the World Bank, the African Development Bank (AfDB), and some primate companies (Grepin, 2015).
20
The rate at which the pledges have been disbursed or committed vary. Some of the examples pointed out in Grepin (2015) show that the United States had disbursed or committed around 95
percent and the World Bank, around 50 percent. It should, however, be noted that the World Bank and other multilateral organizations have extended loans to the affected countries.
19
28
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
the epidemic has destroyed lives and livelihoods.
The slow pace of converting pledges to
commitment has been mentioned as one of the
factors militating early containment. Fulfilling
the pledges is vital to ending the pandemic and
accelerating early recovery. An important lesson
is the urgent need to establish a mechanism that
allows rapid disbursement of funds during public
health crises.
The pandemic has created groups of heroes. The first
is the group that stayed with the epicentre countries
at the most challenging period – the UN agencies
and the international NGOs such MSF. The second
group relates to countries and organizations that
made appreciable financial resources available.
The largest donors as of 17 February 2015 include
the United States of America (US$937 million),
the United Kingdom (US$330 million), Germany
(US$172 million), the World Bank (US$137 million)
and France (US$108 million). Bilateral donors
(e.g. USA, UK and Germany) accounted for about
60 percent of the donations, followed by multilateral
organizations (e.g. the World Bank and AfDB)
(11.5%), private individuals and organizations
(10.0%), foundations (8.3%) and companies (6.6%).
The third is the group of several countries (e.g.
Cuba, China, Uganda, Nigeria and Ethiopia) that
sent health workers to the affected countries.
The destination of the pledges is not restricted
to the three epicentre countries. Some of the
donations have been targeted at the West African
region (around 40 percent), while several are not
country-specific because of the multi-countries
presence of donors and NGOs. A substantial
part of the pledges has been sent to the three
epicentre countries, with Liberia receiving the
largest (US$882.0 million), followed by Sierra
Leone (about $500 million) and Guinea (about
21
$250 million). Financial support was also
pledged to non-epicentre countries to handle
preparedness activities.21
Many recipients benefitted from these resources:
UN agencies (especially WHO and UNICEF)
(42.6%), NGOs (18.9%), governments (11.5%),
Red Cross/Red Crescent (9.9%, private organizations
and foundations (4.4%), and other actors received
the balance (Grepin 2015). The multiplicity of
recipients could be explained by the intensity
and complexity of the pandemic, which make it
extremely difficult for actors to deal with effectively.
However, for the recovery process, government
and local actors will have to play a very strong
role. The international organizations should work
with governments to mobilize resources for the
recovery process. The coordination and lead of
governments are vital in the implementation of the
recovery process for sustainability, ownership and
capacity building.
The coordination between the national and
regional levels has been strengthened, the provision
of case management centres has improved, and
support to field hospitals for healthcare workers
in the epicentre countries has also been enhanced.
Nevertheless, and in spite of enhanced coordination
from the United Nations, there is much room for
improvement from the international community.
To date, the response to this rapidly changing
epidemic has been inadequate, delayed and sparse,
with uncoordinated efforts, limited interventions
by experts and numerous unfulfilled promises.
Most supports in the provision of case management
centres and related facilities are concentrated in
urban areas, especially in Liberia, and many part
of remote and heavily affected communities lack
case management centres even ten months after
EVD was confirmed in the country.
They include Côte d’Ivoire (US$22million), Ghana (US$14.0 million), Mali (US$11.0 million), Nigeria (US$4.0 million) and Senegal (US$3.0 million). See Grepin (2015) for more information on the
beneficiaries of the pledges.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
29
Figure 12: Pledges, commitments and disbursements for the UN Ebola Plan, as of 27 October 2014
Funded Pledge
Unfunded Pledge
Funding Gap
218
279
493
105
93
92
216
63
45
World
Bank
United
States
AfDB
63
39
61
51
51
29
Private United Canada
Donors Kingdom
51
33
30
10
12
19
19
15
15
15
Japan Sweden Germany Netherlands CERF
15
15
13
13
UN Australia
Agencies
11
India
10
10
10
35
Finland European Other
Commission Donors
Sources: Salaam-Blyther (2014) and UNOCHA (2014a).
The situation on the ground shows a147
case of the
‘double failure’ phenomenon. The slow response
at the initial stage of the epidemic is now being
compounded with partners’ inability to adapt to
the current needs based on reality in the field. As
highlighted by MSF (2014a), some international
agencies are allocating resources to activities that
are no longer appropriate to the current situation.
The allocation of case management facilities by
many international organizations when there is
strong evidence of adequate isolation capacities
and a drop in EVD cases in Monrovia is a good
example. Such resources could have been taken
to remote areas where they are mostly needed
(such as Bong, Margibi, Gbarpolu, Grand Cape
Mount and River Cess counties)22 or be used to
train personnel managing the Case Management
Centres (CMCs) who had not been adequately
trained. This could also be used to kick-start the
socio-economic recovery process. It is therefore
important to adapt quickly enough to this rapidly
changing situation and allocate resources to where
they are needed the most.
22
30
The international responses to the Ebola outbreak
have raised several questions regarding global
health governance structures, international
commitment to bolstering pandemic preparedness
and response capacity in poor countries, and global
support for strengthening health systems. This
development shows that the global community
is not ready to address a virulent pandemic like
Ebola. It therefore calls for the urgent need to
rethink the global health management system.
2.5 UN System response to the Ebola
virus disease
While most partners withdrew at the peak of
the crisis, United Nations agencies not only
stayed, but also increased their presence in the
epicentre centres (Guinea, Liberia and Sierra
Leone) and the corridor countries (Senegal and
Ghana). This further rendered the United Nations
System a trusted partner in West Africa. Prior
to the emergence of EVD, the United Nations
agencies operating in the West African region
A good example of this is the construction of a 100-bed CMC in Monrovia, where there were already 580 operational beds in four existing CMCs compared to only 178 operational beds in CMCs
in the rest of the country. There was a plan to open another two in the same neighbourhood (MSF, 2014a).
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
were preoccupied with the overarching goal
of addressing poverty and inequality in all its
manifestations and promoting sustainable human
development. Due to the spread of Ebola, different
the United Nations agencies are confronted
with extremely difficult trade-offs between the
overriding need to stop the epidemic as soon
as possible, the urgent need to prevent affected
communities from sinking further into poverty,
and the obligation to maintain efforts to achieve
long-term development. Thus, programmes need
to be modified to find the right balance between
these goals and avoid focusing all interventions on
the fight against the epidemic. Indeed, a relaxation
of development effort could be highly detrimental
to recovery once the epidemic has been brought
under control.
Since the discovery of the first EVD case in
Guinea in December 2013, the United Nations
System has responded strongly to support the
affected countries to fight the disease. Moreover,
the declaration by the WHO that the EVD was “a
public health emergency of international concern”
and the United Nations Security Council’s
declaration that EVD in West Africa was a “threat
to international peace and security” substantially
reinforced the global partnership on the crisis.
The UN agencies have helped in estimating the
direct and indirect costs of the pandemic, which has
provided some operational guidance to countries.
For instance, in August 2014, the WHO estimated
that it would cost roughly US$500 million to
contain the outbreak by January 2015. It also
estimated the unit cost of testing one person for
Ebola at US$244. For every reported case, health
workers need to trace at least ten people whom the
patient may have contacted, which costs around
US$225 per person. When infected individuals
23
die, their bodies must be safely buried and their
households sanitized without workers risking
infection, which costs US$404. Hence, given the
reported 20,381 cases and 7,889 deaths in early
January 2015 across the three affected countries,
the estimated, partial direct cost of the epidemic is
currently around US$50 million. However, as the
cost of treating patients is difficult to ascertain and
varied, the total direct cost is difficult to estimate.
The United Nations Mission for Ebola Emergency
Response (UNMEER), established to utilize the
assets of all relevant UN agencies in addressing the
health and broader social impacts of the outbreak,
also estimated that the UN response would cost
around US$1.0 billion (see Table 5), around half
of which would be aimed at addressing health
impacts. OCHA, using the Inter-Agency Response
Plan for Ebola Virus Outbreak, specified the
overall needs and requirements (ONR) of US$1.5
billion. As of December 8, nearly two thirds of
this amount had been met through response plan
funding. The revised ONR, covering October 2014
to June 2015, is estimated at US$2.27 billion.23
According to ECA (2014), the contributions of
multilateral organizations and bilateral partners
may far exceed the initial financial requirement,
even if only a small amount of the pledged amount
has been disbursed.
Most UN agencies have reprogrammed their
interventions in the epicentre countries because
of the outbreak. Their new prioritization includes:
communicable diseases, non-communicable
diseases, and issues dealing with women’s health,
polio and emergency response (WHO); using
school meals programmes to support quarantine
households, relief and recovery operations,
emergency food assistance and support for
This includes US$1.5 billion from October 2014 to March 2015 (initial ONR requirements) and $0.768 billion for April to June 2015 of the US$1.536 billion. See OCHA (2015), available at:
http://fts.unocha.org/pageloader.aspx?page=emerg-emergencyDetails&emergID=16506.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
31
farmers’ income as well as food assistance to
the affected people (WFP); a joint provision of
65,000 tonnes of food assistance to approximately
1.3 million of the most affected people (WFP and
FAO); and building capacity in the government,
supporting fund management capacity, supporting
the hazard payroll system for the Ebola workers, the
deployment of SURGE capacities, decentralization
of sectoral governance and coordination of
Ebola recovery support as mandated by the UN
Secretary-General (UNDP), among other areas
being handled by other UN agencies (UNICEF,
2014; UNDP-RBA, 2014a). UN Women is working
with the Ministry of Social Welfare, Gender and
Children’s Affairs (MSWGCA) to mainstream
gender into a number of key EVD interventions,
providing psychosocial support to affected
families and communities in de-stigmatizing and
reintegrating the survivors of EVD back into the
communities, and supporting affected women
and girls in both treatment and isolation centres
with basic hygiene materials and awareness
raising through intensive radio programmes. The
United Nations Children’s Fund (UNICEF) is
exploring traditional and new ways to help provide
children with the physical and emotional healing
they need. It is working with local authorities in
the most affected counties to help strengthen
family and community support to children
affected by Ebola and to provide care to those who
have been rejected by their communities or whose
families have died. In Sierra Leone, it has trained
about 2,500 survivors to provide care and support
to quarantined children in treatment centres and
works with partners to reunite separated children
with their families through an extensive family
tracing network across the country.24 United
Nations Office of High Commissioner for Human
Rights (OHCHR, 2015) conducted an analysis of
24
32
Some of the UNICEF response actions are discussed in www.unicef.org/media/media_76085.html
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
the human rights dimension, including in terms
of responses to the epidemic. It also monitored
the human rights impact of the outbreak and
promoted integrating human rights into responses
in the most affected countries.
Building on strengthened coordination to fight
the EVD, the UN agencies should work with
national and international partners to address the
root causes of the epidemic, rather than focusing
all efforts on the present crisis. One key goal of
United Nations programmes should be to help
the governments of these countries to strengthen
health management coordination and help build
strategic partnerships around the health system
development.
The United Nations Development Assistance
Framework (UNDAF) for West African countries
should be concerned with strengthening the
health services’ capacity to cope with future
epidemics without compromising the fight
against other priority diseases, and ensuring the
provision of quality care. Emphasis should be
placed on the establishment of a National Health
Development Plan that takes into account the
lessons learned from the Ebola epidemic and
guides the reconstruction of a system capable of
resisting similar shocks. Specific interventions
from UN agencies such as WHO, UNICEF, United
Nations Population Fund (UNFPA) and the
World Bank, among others, should give priority
attention to the implementation of this strategic
action. This also calls for preventive measures,
especially in countries with a high probability of
EVD occurrence, as predicted in this report, for
example, The Gambia, Ghana and Côte d’Ivoire.
These measures include developing early warning
plans and strategies.
Table 5: United Nations Ebola Response Plan (US$ million)
Comon Services
Guinea
Liberia
Sierra Leone
Total
26.8
116.5
39.2
189.5
4.3
14.2
4.4
23.8
(1) STOP THE OUTBREAK
Case identification and contact tracing
7.0
Safe and dignified burials
0.8
(2) TREAT THE INFECTION
Ebola care and infection control
7.0
52.5
212.6
59.2
331.2
Medical care for responders
10.0
1.0
2.0
1.0
14.0
(3) ENSURE ESSENTIAL SERVICES
Food aid
2.5
28.4
36.3
40.4
107.7
Basic health care
1.0
47.1
12.9
36.1
97.1
Cash incentives for health workers
0.0
2.5
0.0
0.0
2.5
Recovery and economy
0.3
9.5
43.1
11.7
64.8
(4) PRESERVE STABILITY
Supply chain management
3.9
3.1
20.7
14.8
42.6
Transport and fuel
22.9
0.0
0.5
0.0
23.4
Social mobilization
0.6
18.6
13.2
13.3
45.8
Messaging
1.5
0.3
1.1
0.3
3.2
Regional support for Points 1-4
11.9
0.0
0.0
0.0
0.0
(5) PREVENT OUTBREAKS IN UNAFFECTED COUNTRIES
Multi-faceted/preparedness (regional)
30.5
0.0
0.0
0.0
30.5
Total
88.0
194.1
473.1
220.4
987.8
Source: UNOCHA (2014b).
Concerted and urgent efforts should focus on
resuscitating livelihoods that were destroyed by the
EVD. Transitional support to purchase inputs is
essential to prepare for the next crop, while adding
cash transfers to food aid (for a limited period)
would help communities facing food insecurity to
reconstitute their means of sustainable livelihoods.
This also includes the opening of trade corridors
in the continent. The early recovery plan and
strategy25 being developed by the UN System
should give priority attention to this. The United
Nations Industrial Development Organization
(UNIDO) and UNDP should work on the
industrial component of the agricultural value
chain including supporting the strengthening
25
of the microcredit schemes and empowering
small-scale businesses. Giving priority attention to
addressing the structural causes of the fragility at
the national and regional levels, especially for the
MRU, is also pivotal.
Although the epicentre countries have initiated
some recovery plans, and the UN agencies are
also consolidating their efforts, partnerships with
the national governments and ECOWAS on the
long-term approach to addressing the crisis is
critical. The UN system could help the governments
and ECOWAS design national and regional
recovery plans, in conjunction with sources of
financing and partnership building. Based on
Many United Nations agencies are involved, including: World Bank, Food and Agricultural Organization of the United Nations (FAO), International Fund for Agricultural Development (IFAD), World
Food Programme (WFP), UNDP, UNIDO, International Labour Organization (ILO), United Nations Economic Commission for Africa (UNECA), WHO and UNICEF.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
33
the UN Secretary-General’s Memorandum of
12 December 2014 on the ‘Recovery initiative
in Ebola-Affected Countries’, UNDP has been
mandated to lead the UN recovery process in
close consultation with other agencies. Effective
coordination of the recovery programme is central
to accelerating progress.
The United Nations also has a role to play with
national and regional governments to follow
up on the pledges made to alleviate the crisis.
As of January 2015, more than 983 pledges
(international development agencies, donors,
NGOs and private sector organizations) have
been made, 433 of which to the UN agencies. As
of January 2015, only about 111 agencies have
fulfilled their pledges. Indeed, of US$2.27 billion
needed for the Ebola Response Plan, as indicated
by United Nations Office for the Coordination of
Humanitarian Affairs (UNOCHA), only US$1.25
billion, around 55 percent, has been covered.
Substantial resources are still needed to fill the gap.
Resource mobilization is key to moving forward
on the Ebola response interventions.
In terms of organization, the number of agencies
represents an important challenge. Coordination
failure and competition between them are causes
of concern that should be addressed so that the
United Nations can continue to enjoy the trust
of the West African governments. The Regional
Directors’ Team provides a good opportunity for
coordination. The outbreak of the disease created a
great organizational challenge, to which the United
Nations responded by setting up the first-ever UN
mission for a public health emergency, UNMEER.
34
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
The primary task of the new agency is to coordinate
the UN agencies’ vast resources to combat the
epidemic under the technical leadership of the
WHO. The new coordination agency has been based
in Accra, Ghana. However, its main challenge is to
ensure implementation coordination. UNMEER
is working with the WHO to develop indicators,
based on the availability of human resources and
treatment centres, for monitoring Ebola response
efforts. Irrespective of the role assigned to each
organization, UN agencies should work as a team
in helping these countries to recover. The synergy
of UN efforts is key to accelerating progress.
To solve the coordination failure of UN agencies,
UNMEER should design a strong division of
labour matrix and ensure its implementation.
A committee should be set up to receive and
redirect any pledge. In addition, this committee
should ensure that duplication and unhealthy
competition on the ground by United Nations
agencies is avoided.
Women vegetable farmers and traders in Sierra Leone have faced important losses due to restrictions on the transportation of goods and periodic markets bans in the context
of the Ebola outbreak. Photo by FAO Guinea/K. Jawad FAO/Keifa Jaward – Communication officer in Sierra Leone
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
35
A Malian woman packaging mangoes for export. The Malian economy has felt the impact of the Ebola crisis. Photo by UNDP/Mali
The macro-economic impact
of the Ebola virus disease
3.The macro-economic impact
of the Ebola virus disease
3.1.Overview of relevant pandemic
modelling approaches
With the deadly spread of the EVD, the global
interest in the disease has increased since 2014.
While it is understood that most of the focus is on
public health systems, it is instrumental to think
about what the affected countries will do to recover
after the epidemic is contained. Hence, the analysis
of the macro-economic impact becomes the next
step. However, such an estimation is not an easy
task. Few studies have assumed the economic
impact of pandemics such as Severe Acute
Respiratory Syndrome (SARS), influenza, malaria
or HIV/AIDS.
Kennedy, Thomson and Vujanovic (2006) assessed
the macro-economic effects of an influenza
pandemic of the Australian economy using the
Treasury Macro-economic (TRYM) model. The
scenario modelled in their study is of a nationwide
outbreak of a highly contagious influenza virus with
a mortality rate of 0.2 per cent of the population,
or 40,000 deaths nationwide. The model allowed
the estimation of the growth rate at the national
level for only the first year. Cuddington and
Hancock (1993a and b) used a modified Solow
Growth Model to stimulate the impact of the AIDS
epidemic on the growth path of the Malawian
economy. They found a 0.2-0.3 decrease of GDP
growth over the 1985-2010 period.
The high death rate and the contamination speed
have elicited several evidence-based studies
on the EVD in West Africa. In 2014, there were
four major contributions to the assessment of
the macro-economic impact of the EVD in West
Africa (World Bank, 2014; UNECA, 2014; and
26
UNDP-RBA, 2014a and 2015).26 In October 2014,
World Bank published a study on the economic
impact of the 2014 EVD. Using a dynamic
computable general equilibrium (CGE) model
for Liberia, the study provided a medium-term
estimation of the impacts of the EVD in growth
rates. For Guinea and Sierra Leone that have no
computable general equilibrium (CGE) model, the
World Bank study used available data to estimate
the change in projected growth rates by sector
in order to calculate the updated change in the
growth rate. To estimate the impact for the West
African region, this study used CGE modelling,
more specifically the LINKAGE model, which
draws on the Global Trade Analysis Project
(GTAP) database of economic transactions within
and across economies for 2013.
In December 2014, UNECA published a
comprehensive study on the impacts of the EVD
on the economies of the three affected countries
(UNECA, 2014). The study used four approaches
for this assessment: (i) a descriptive quantitative
analysis; (ii) a survey on non-affected countries’
preparedness and on indirect effects of the EVD;
(iii) an analysis of the continental impact of the
EVD; and (iv) a perceptions analysis by statistical
text mining. Based on these methodologies,
UNECA study found impacts of the EVD in
various sectors on the countries’ economies. This
study is one of the first to assess the impact in
various sectors of these economies.
UNDP published two different studies on the three
epicentre countries using a multi-dimensional
approach (UNDP-RBA, 2014a and 2015a). It
undertook field surveys in the three most affected
A major constraint to such studies is the availability and quality of data.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
37
countries to provide evidence for building
assumptions for the macro-models using both
macro-econometrics and CGE models. It provided
a medium-term (2014-2017) estimation for
key macro-economic and social variables. The
endogenous growth and dynamic CGE model
was employed for the study. The two approaches
provide complementary results.
While these studies provided some estimations of
GDP reduction for each of the three most affected
countries, the estimation for the West African
region is carried out at the aggregate level. In
addition, the impact on poverty is not analysed,
not even by calculating the GDP per capita.
The UNDG-WCA report, building on earlier
studies, is an attempt to provide further insight
into the impact of the EVD. It is the first report
that will model the socio-economic impact of
EVD for the 15 West African countries. The
earlier studies either modelled the three epicentre
countries or provided a rough estimate for the
region. Also, this report discusses the EVD
incidence and food security at the country level.
In addition, this report uses a macro-economic
model that has not previously been used to assess
the impact of the EVD, except for UNDP-RBA
(2014a and 2015a), but only for the three epicentre
countries. Hence, this report confirms some of
the results of previous studies. Finally, it uses the
latest epidemiological results from the field for the
model, which are more accurate than those based
on relatively dated assumptions and data.
3.2.Methodology
In general, the epidemic will impact the economy
through three channels: direct, indirect and
deferred indirect costs. The direct costs are mostly
medical expenditures linked to the EVD at the
38
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
macro level. At the household level, the most
direct cost is the use of family savings to take care
of an EVD-infected family member. Indirect costs
mainly consist in a reduction of productivity (or
labour participation) for those who survived,
or lack of productivity for those who did not
survive. The deferred indirect cost is the cost that
the household will have to pay in the absence of
external aid. The epidemic may destroy the fragile
economic structure. Children have not been able
to attend school, and the long-term impacts will
be devastating.
The model used in this report will capture these
three effects at the aggregate level. This section
summarizes the main model used to assess the
impact of the EVD in West Africa and presents the
results of the empirical estimations.
The macro-economic impact model for the
Ebola virus disease
To assess the macro-economic impact of the
EVD, two different modelling schemes were used,
derived from previous studies on HIV/AIDS. The
first model considered is the dual macro-Ebola
model. It allows to analyse, in the context of the
Solow-Swan growth model, the macro-economic
impact of a temporarily shock on the labour force
and capital accumulation caused by the Ebola
epidemic. The macro-Ebola model is designed as
in Cuddington and Hancock (1993a and b). In
this scheme, the path of economic indicators is
simulated under a scenario of no Ebola and then
under a scenario with presence of EVD cases. The
former represents a no-EVD benchmark against
which any EVD scenario might be compared.
Although different parameters are assumed, the
simulation mainly focuses on the impact of lost
labour productivity per EVD case.
A cross-sectional empirical model was used for the
estimation. The aim is to estimate the coefficient
of a variable that captures the potential impact of
EVD prevalence on an economy. To this extent, a
non-linear model similar to the one designed in
Bloom and Mahal (1995) is used. The transmission
of an epidemic disease is determined by different
factors, including economic conditions. Having
realized that this might create issues with
simultaneity bias, the models use non-linear
two-stage least square techniques.
This model is different from most of the
epidemiologic models found in the literature.
Indeed, for the EVD, the first symptoms appear
about 21 days after being infected, and infected
workers may be completely or partially unable to
work, which affects their families and communities.
In the meantime, they may have contaminated other
workers and family members. To avoid further
contagion, workers are isolated at the appearance
of the first symptoms. With the high fatality rate,
the chances of returning to work are minimal.
Hence, one of the main impacts of the EVD is the
huge reduction of labour supply. In our model, we
estimate the production per capita for the case with
and without EVD, to enable the estimation of the
cost of the EVD on the economy (see Annexes 1
and 2 for the detailed methodology).
3.3.Key findings from
empirical results
3.3.1 Results of the probability distribution
of Ebola virus disease prevalence
This report simulates the probability of having
Ebola cases under low and high scenarios for each
of the 15 West African countries between 2013
and 2017. Annex 1b provides the assumptions and
equations for estimating the probabilities.
27
Based on the results, countries are grouped into
four categories of probability of EVD prevalence.
The first category is the high probability of EVD
prevalence in the three epicentre countries,
which is expected according to the current
local situation. Second, Ghana and The Gambia
also have a relatively high probability of EVD
prevalence – 0.4 and 0.6, respectively. This calls
for the governments in these countries to initiate
pro-active preventive measures, including early
warning mechanisms. Third, there are countries
whose probability of prevalence ranges between
0.1 and 0.2 (Côte d’Ivoire, Benin, Togo, Burkina
Faso and Nigeria). And finally, countries such as
Cape Verde, Niger, Guinea Bissau, Senegal and
Mali have very low probability. The results for
Mali, Guinea Bissau and Senegal contradict the
general expectation given the contiguity of these
countries to the epicentre countries. This may be
as a result of strong preventive measures against
the EVD. (See Annex 3 for the specific results for
each of the countries.)
3.3.2 Trade impacts of Ebola virus disease
Due to the restriction of the epidemic to certain
countries, trade appears to be the most important
variable for examining the effects of the disease in
West Africa. The sub-regional trade indicates that
Guinea, Liberia and Sierra Leone contribute only
marginally to intra-ECOWAS trade. The share of
the three countries in the sub-regional trade is very
low. Thus, the border closures and the isolation do
not significantly affect the volume of trade in the
West African region.27 Indeed, over the 2010-2013
period, the three countries account only for 1.73
percent of imports and 1.39 percent of intra-EU
exports annually.
While this may be the case at the macro and formal levels, the situation for people who depend on informal cross- border trade may be different. It is important, therefore, to undertake some
micro-assessments on this issue to further examine the detailed impact on people whose livelihoods depend on cross-border trade.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
39
Over the same period, Guinea contributed an
annual average of 1.25 percent in to intra-ECOWAS
imports against 1.23 percent for exports. Its share
in total intra-ECOWAS imports varies from
1.56 percent in 2010 to 1.34 percent in 2013,
against 2.25 percent and 0.16 percent for exports,
respectively. These rates are even lower for Liberia
and Sierra, remaining below the 0.3 percent of
total intra-ECOWAS imports and exports. For
this reason, a drastic reduction of trade within the
region is not expected. Tables 6 and 7 provide the
trade statistics for other countries.
Table 6: Share of countries in intra-ECOWAS trade
Intra-ECOWAS Imports
Country
2010
2011 prev
2012 prev
2013 prev
2010
2011 prev
2012 prev
2013 prev
Benin
0.40
0.17
0.20
0.39
3.42
0.44
0.65
0.83
Burkina Faso
1.17
1.33
1.18
1.77
1.70
0.83
0.99
1.22
Cape Verde
0.20
0.17
0.18
0.25
0.02
0.04
0.01
0.04
Côte d'Ivoire
9.14
6.30
5.88
10.31
35.39
14.89
21.82
29.10
Gambia, The
0.06
0.05
0.06
0.08
0.51
0.52
0.79
0.65
Ghana
5.74
13.53
9.99
12.00
9.20
42.07
14.35
9.50
Guinea
1.56
1.04
1.08
1.34
2.25
1.08
1.45
0.16
Guinea Bissau
0.06
0.04
0.04
0.08
0.01
0.01
0.02
0.00
Liberia
0.20
0.21
0.25
0.42
0.04
0.02
0.03
0.02
Mali
1.77
1.36
1.42
1.88
3.04
2.32
2.42
1.94
Niger
0.36
0.21
0.22
0.38
2.70
1.17
2.84
3.00
Nigeria
77.00
73.58
77.46
68.10
28.32
27.95
43.53
41.95
Senegal
1.76
1.37
1.34
1.89
11.73
5.34
6.72
6.52
Sierra Leone
0.09
0.23
0.18
0.35
0.10
0.16
0.03
0.22
Togo
0.50
0.40
0.49
0.75
1.58
3.15
4.36
4.85
Total
100
100
100
100
100
100
100
100
Source: ECOWAS Commission.
40
Intra-ECOWAS Exports
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Table 7: Member Countries’ share of intra-ECOWAS imports and exports in foreign trade
Intra-ECOWAS Imports
Intra-ECOWAS exports
Country
2010
2011 prev
2012 prev
2013 prev
2010
2011 prev
2012 prev
2013 prev
Benin
54.5
22.6
22.2
23.2
14.7
18.9
17.4
12.1
Burkina Faso
9.4
5.5
5.8
7.5
26.7
24.6
22.6
21.4
Cape Verde
0.6
2.2
0.4
1.8
1.4
0.8
1.2
1.1
Côte d'Ivoire
24.9
21.1
25.6
30.5
28.0
25.9
28.7
25.2
Gambia, The
54.0
86.8
84.4
88.9
23.2
26.6
33.0
29.3
Ghana
10.3
27.8
9.9
8.6
7.9
7.8
4.4
1.6
Guinea
9.3
9.3
9.3
1.3
4.0
4.0
4.0
1.3
Guinea Bissau
0.7
2.6
2.6
0.1
16.3
17.6
17.6
10.8
Liberia
1.5
1.0
0.8
0.5
0.7
0.4
1.5
2.4
Mali
11.0
15.3
11.7
11.1
30.2
40.4
44.8
33.4
Niger
48.8
50.7
87.5
84.7
11.4
14.7
19.9
20.8
Nigeria
2.4
3.4
3.9
6.7
0.4
1.4
0.4
10.2
Senegal
42.9
34.7
34.6
37.2
13.3
13.6
16.2
14.5
Sierra Leone
6.8
6.2
1.2
6.8
49.9
52.7
32.7
34.9
Togo
20.3
70.5
61.4
69.5
11.0
8.9
10.1
10.2
Source: ECOWAS Commission.
3.2.3 The impact on GDP growth and GDP
per capita growth
The section presents the macro-economic
estimation of the GDP growth. The results are
presented under three scenarios: a baseline
scenario (no Ebola); a high EVD scenario; and a
low EVD scenario. The report presents the analysis
sequentially based on the severity of the EVD. First,
the results for the three countries mostly affected
by Ebola (Guinea, Liberia and Sierra Leone) are
presented. This is followed by a discussion of the
countries that had a positive number of detected
cases but are now Ebola-free (Mali, Nigeria and
Senegal). Finally, the results for the other West
African countries are presented as well as for the
entire West African region.
Guinea. Guinea is the least affected of the three
epicentre countries, with 2,707 cases and a total of
1,709 deaths reported. Recently, several measures
implemented by the Government to contain the
EVD have yielded results. Figure 13 and Table 8
summarize the macro-economic impacts for
Guinea between 2014 and 2017 in terms of GDP
per capita and GDP growth. In the high Ebola
scenario (the current situation), the country will
experience a 3.4 percent reduction in average GDP
growth between 2014 and 2017 relative to the no
Ebola scenario (or baseline). This reduction in
GDP growth represents around US$155.9 million
in lost GDP in 2015 for the low EVD case scenario
and US$238.7 million for the high EVD scenario.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
41
During the 2014-17 period, Guinea’s average
GDP loss is estimated at US$184.4 million each
year, or around 4.9 percent of the average GDP
over the same period in the low Ebola scenario.
In the high Ebola scenario, the average loss of
GDP is US$315.5 million, or around 8.6 percent
of GDP over this timeframe. On the loss of GDP,
Guinea is the least affected country among the
three heavily affected countries. In terms of GDP
per capita, the difference between the baseline
model and the low Ebola scenario is around 1.1
percent, and 1.8 percent in the high scenario. This
is equivalent to a loss of US$9 per capita between
the baseline scenario and the low Ebola scenario.
In the high Ebola scenario, the loss relative to the
baseline is around US$15 per capita.
Table 8: Guinea: Macro-economic impacts of the Ebola virus disease, 2004-2017
2014
2015
2016
2017
GDP change in Low Ebola scenario
-84.7
-155.9
-214.7
-282.3
GDP change in High Ebola scenario
-118.4
-238.7
-388.4
-516.3
(US$ million)
Source: Authors’ estimation.
Figure 13: Guinea: Impact of the Ebola virus disease on GDP growth and GDP per capita growth, 2014-2017
GDP per capita growth (%)
GDP growth (%)
2
4
1
3
0
2
-1
1
-2
2014
2015
2016
0
2017
No Ebola
Low Ebola
2014
2015
High Ebola
Source: Authors’ estimation.
147
42
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Center
Right
left
2016
2017
Liberia. Liberia is the second most affected country
in terms of cases and fatalities. Indeed, according to
the WHO, as of 7 February 2015, around 8,881 cases
were reported, resulting in 3,826 deaths. The impact
of the EVD in terms of GDP loss is around US$159
million in 2015 in the low scenario and US$214
million for the high scenario. On average, the loss
of GDP is estimated at US$187.7 million per year,
or an average of 13.7 percent of GDP during the
2014-2017 period. For the high scenario, the average
loss is around 18.7 percent of the average GDP, or
US$245.2 million annually, over the same period.
In terms of GDP per capita, the loss for Liberia
is on average US$38 per year in the low scenario
and US$48 per year for the high scenario. In the
low scenario, the loss of GDP per capita growth is
around 4 percent compared to around 5 percent
for the high scenario (figure 14 and table 9).
Table 9: Liberia: Macro-economic impacts of the Ebola virus disease, 2004-2017
2014
2015
2016
2017
GDP change in Low EVD scenario
-105.3
-159.0
-221.1
-265.2
GDP change in High Ebola scenario
-135.7
-214.3
-289.1
-341.8
(US$ million)
Source: Authors’ estimation.
Figure 14: Liberia: Impact of the Ebola virus disease on GDP growth and GDP per capita growth, 2014-2017
GDP per capita growth (%)
GDP growth (%)
8
10
6
8
4
6
2
4
0
2
-2
0
-4
-2
-6
2014
2015
2016
-4
2017
No Ebola
Low Ebola
2014
2015
2016
2017
High Ebola
Source: Authors’ estimation.
147
Center
Right
left
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
43
Sierra Leone. Sierra Leone is the most affected
country, with 10,934 cases, including 3,341
reported deaths (as of 8 February 2015). The
lack of data did not allow for a forecast of the
GDP of the country beyond 2015. In terms of
GDP growth, according to the low scenario,
there is a 6-percentage point reduction of GDP
growth in 2014 and 8 percentage points for the
high scenario. In the low scenario, the country
will lose around US$219 million in 2015 in the
low scenario and US$286 million in the high
scenario. Over the 2014-17 period, the country
will lose between US$200.7 million (7.1% of
average GDP) and US$264.3 million for the low
and high scenarios, respectively (figure 15 and
table 10).
Table 10: Sierra Leone: Macro-economic impacts of the Ebola virus disease, 2004-2017
2014
2015
2016
2017
GDP change in Low EVD scenario
-145.1
-219.2
-219.2
-219.2
GDP change in High EVD scenario
-196.7
-286.8
-286.8
-286.8
(US$ million)
Source: Authors’ estimation.
Figure 15: Sierra Leone: Impact of the Ebola virus disease on GDP growth and GDP per capita growth, 2014-2017
GDP per capita growth (%)
GDP growth (%)
14
14
12
12
10
10
8
8
6
6
4
4
2
2
0
2014
2015
2016
0
2017
No Ebola
Low Ebola
2014
2015
High Ebola
Source: Authors’ estimation.
147
44
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Center
Right
left
2016
2017
Senegal. Senegal is the only country of the region
with only one case of EVD and no deaths. The
impact on GDP growth in Senegal is only 0.6
percentage points in 2015 in the low scenario and
0.9 percentage points in the high scenario. The
impacts are mostly due to the indirect impacts
of the EVD on the country.28 The country will
lose 1.1 percent of the average GDP (US$145.2
million) over the 2014-17 period in the low Ebola
scenario and 1.7 percent (US$221 million) in the
high scenario (figure 16 and table 11).
of US$81.6 million per year. In the high Ebola
scenario, the country will lose 0.6 percentage
points of GDP on average, or US$164.6 million
per year.
Nigeria. With 20 reported cases and eight deaths,
Nigeria is the most affected country outside the
epicentre countries. The impact in terms of lost
GDP growth due to the EVD is 0.1 percentage
points per year in the low Ebola scenario, i.e.
an average loss of US$1.4 billion (or 0.7% of
the average GDP). In terms of GDP per capita,
US$22 is lost between the baseline and the low
Ebola scenario. In the high Ebola scenario, GDP
growth is reduced by 0.2 percentage points, and
the country will lose around US$1.6 billion. GDP
per capita will be further reduced by US$48.
Mali. In Mali, six cases of EVD have been reported,
all of which were fatal. In the low scenario, the
macro-economic impact of the outbreak in
terms of GDP growth is 0.3 percentage points
between 2014 and 2017. Mali will lose on average
Table 11: Senegal, Mali and Nigeria: Macro-economic impacts of the Ebola virus disease, 2004-2017
2014
2015
2016
2017
(US$ million)
Senegal
GDP change in Low EVD scenario
-30.8
-106.5
-206.8
-236.7
GDP change in High EVD scenario
-64.6
-177.9
-297.9
-347.0
Mali
GDP change in Low EVD scenario
-46.1
-75.1
-99.4
-105.6
GDP change in High EVD scenario
-135.4
-239.1
-293.1
-317.1
GDP change in Low EVD scenario
-890.8
-1,898.6
-2,220.9
-1,167.1
GDP change in High EVD scenario
-801.8
-1,903.3
-2,386.8
-1,567.4
Nigeria
Source: Authors’ estimation.
28
While this may be the case at the macro and formal levels, the situation for people who depend on informal cross- border trade may be different. It is important, therefore, to undertake some
micro assessments on this issue to further examine the detailed impact on people whose livelihoods depend on cross-border trade.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
45
Figure 16: Senegal, Mali and Nigeria: Impact of the Ebola virus disease on GDP growth, 2014-2017
Senegal
Mali
10
10
8
8
6
6
4
4
2
2
0
2014
2015
2016
No Ebola
Nigeria
0
2017
Low Ebola
2014
2015
2016
2017
2016
2017
High Ebola
10
8
6
147
Center
Right
left
4
2
0
2014
2015
2016
2017
No Ebola
2014
Low Ebola
2015
High Ebola
Source: Authors’ estimation.
147
46
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Center
Right
left
Figure 17: West African countries not directly affected by the the Ebola virus disease: Impact of the Ebola virus
disease on GDP growth, 2014-2017
Benin
Burkina Faso
6
6
5
5
4
4
3
3
2
2
1
1
0
2014
2015
2016
Côte d’Ivoire
0
2017
No Ebola
Low Ebola
3
8
2.4
147
Center
1.8
Right
4
1.2
2
0.6
0
2014
2015
2016
0
2017
No Ebola
2015
2016
2017
High Ebola Cape Verde
10
6
2014
Low Ebola
left
2014
2015
2016
2017
High Ebola
Source: Authors’ estimation.
Benin, Burkina Faso, Cape Verde, Côte
Center
147
d’Ivoire, Ghana, Guinea Bissau, The Gambia,
Niger and Togo. The remaining West African
countries are EVD-free. The indirect impact of
the disease is not negligible, however, because
of the interconnections among the economies of
Right
left
this region. The impact on the GDP growth varies
between 0.1 percentage points and 4 percentage
points (figure 17).
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
47
Figure 18: West African countries not directly affected by the Ebola virus disease: Impact of the Ebola virus disease
on GDP and GDP growth (%), 2014-2017
Ghana
The Gambia
10
25
8
20
6
15
4
10
2
5
0
2014
2015
2016
Guinea Bissau
4
0
2017
No Ebola
Low Ebola
2015
High Ebola
8
3
2016
2017
2016
2017
2016
2017
Niger
6
147
2
Center
1
0
2014
Right
4
left
2
2014
2015
2016
Togo
0
2017
No Ebola
2014
Low Ebola
2015
High Ebola
12
9
147
6
Center
Right
left
3
0
2014
2015
2016
2017
No Ebola
2014
Low Ebola
2015
High Ebola
Source: Authors’ estimation.
48
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
147
Center
Right
left
The West African region: In 2015, under the
low scenario, GDP growth will be 0.5 percentage
points lower. The loss of GDP for the whole
region will be US$3.6 billion on average per year
(i.e. 1.2% of the average GDP of the region). In
impact of the EVD will reduce GDP per capita
growth, by 0.8 percentage points on average
between 2014 and 2017. Hence, the region as a
whole will lose about US$18 per capita per year.
In the high scenario, GDP growth decreases by
0.5 percentage point on average between 2014
and 2017, which represents a loss of GDP of
around US$4.9 billion per year (see table 12 and
figure 19).
Table 12: West Africa region: Macro-economic impacts of the Ebola virus disease, 2004-2017
2014
2015
2016
2017
GDP change in Low Ebola scenario
-1,799.8
-3,411.7
-4,750.0
-4,697.0
GDP change in High Ebola scenario
-2,317.3
-4,426.4
-6,230.4
-6,691.6
(US$ million)
Source: Authors’ estimation.
Figure 19: West African region: Impact of the Ebola virus disease on GDP growth and GDP per capita growth,
2014-2017
GDP per capita growth (%)
GDP growth (%)
8
8
6
6
4
4
2
2
0
2014
2015
2016
0
2017
No Ebola
Low Ebola
2014
2015
2016
2017
High Ebola
Source: Authors’ estimation.
147
Center
Right
left
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
49
Vendor Struggling with plummeting sales in West Point after the Ebola Outbreak in Liberia. Photo Credit: UNDP Liberia/Caly Learson. Photo by UNDP Liberia/Caly Learson
The socio-economic impact
of the Ebola virus disease
4.The socio-economic impact of the
Ebola virus disease
The socio-economic impact focuses on three
key issues: the anthropological dimension
of the EVD using a case study from Guinea,
its implication on food security, and poverty
impacts. The econometric analysis was used to
establish the food security and poverty impacts.29
The UNDP national studies in Guinea, Liberia
and Sierra Leone provided additional primary
sources of information and complemented the
anthropological survey from Guinea and other
secondary sources of information.
4.1.The anthropological dimension
An anthropological survey was undertaken to
capture people’s practical experiences of the
socio-economic impact of the EVD in Guinea.
To achieve this, 567 individuals were interviewed,
of whom 33.7 percent were female.30 The sample
cut across people from different age groups –
ranging from 6 to 72 years old, with an average
age of 36 years old: around 59 percent were
35 years old or under; and only 3.7 percent over
65 years old. The results from the survey provide
additional illumination to the macro-economic
modelling results.
People surveyed view the impact of EVD on the
Guinean economy and households’ economic
activities as negative. Respondents stressed that
that the outbreak had a strong negative impact
on the labour market. Seven out of ten people
believed that the labour market shrank. Many
workers reduced their weekly hours of work and
some decided to temporarily stay at home to
prevent themselves from Ebola contamination.
Those seeking new jobs or lacking revenues had
left contaminated localities and moved to less risky
areas to find new jobs (figure 20).
The EVD has slowed down the Guinean economy,
as affirmed by eight out of ten respondents
(figure 20). A combination of several factors
such as the closing of borders, private businesses
and enterprises, awareness of the need to avoid
Figure 20: Perceptions of changes in the labour market and the overall economy
Perception of change in labour
market participation
Perception of signs showing that
people moving to find new jobs
Perception of change
in the overall economy
A little 7%
More 2%
Less 70%
Don’t Know
5%
No 12%
No 30%
Same 23%
Yes 70%
Yes 81%
Source: Authors’ estimation.
Using the results of macro-economic modelling to evaluate the impact of the epidemic on GDP per capita, this report examines the effect on food security and poverty variables. Considering the rate of
economic growth in three hypothesis, namely: (i) the economic development of the countries with a no Ebola scenario; (ii) changes under a low Ebola scenario; and (iii) changes with a high Ebola scenario,
the impact of the epidemic is calculated by determining the difference between the evolution of food security and poverty indexes in the baseline case and each of the low and high infection cases.
30
Efforts to increase the share of women respondents did not yield maximum results due to the severe impact of the pandemic and the feeling of survey fatigue that has started to emerge in the region
selected for the survey. They were adequately represented in the three national studies that were used to complement this survey.
29
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
51
contacts with others, and fear of the disease
and stigmatization have impeded households’
economic activities. Few business holders that
were able to open their businesses have taken
advantage of the scarcity of goods by raising prices.
The inability to carry out their normal businesses
led to a decrease in household incomes.
The feedback on the impact of the EVD on food
security overwhelmingly supports the findings
from the modelling approach. It not only reduces
access to food (affirmed by 90 percent of the
respondents), but has also negatively affected
people’s food consumption habits. Many people
changed their consumption habits and had to eat
less than before the EVD.
The EVD has had a significant impact on the
education sector. The impact consists in the loss of
teachers and students, the closure of schools and
a reduction in school attendance. Respondents to
this survey have known, on average, four teachers
or school staff who have died from the EVD. Nine
out of ten people confirmed the reduction in
school attendance as a result of the EVD (figure
21). This development confirms the issues raised
in the earlier part of the report on the impact of the
EVD on children.
Communal support to the affected people still
remains strong although the social life seems to
be changing. Despite the fear of contracting the
disease, the majority of the respondents affirm
Figure 21: Change in school attendance since the onset of the Ebola virus disease
Don’t know 2%
More attendance 5%
More
attendance 1%
Less attendance 92%
Source: Authors’ estimation.
Figure 22: Acceptance in adopting or taking care of children whose parents have died of the Ebola virus disease
Don't know
No
YES
363
351
2
See UNDP (2015) on how EVD increased women’s vulnerability to loss livelihoods and incomes in the three countries.
128
55
Whose parents are victims of EVD
Source: Authors’ estimation.
52
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
106
57
Who are cured of EVD
that people are ready to welcome or to take care
of children who have been cured of EVD or whose
parents have died of Ebola (figure 22). Yet, the fact
that around one third of the respondents are ready
to distance themselves from the culture of ‘be your
neighbours’ keeper’ is an indication that EVD is
eroding age-long communal behaviours.
The EVD has negatively affected Guinean social
life, including attending ceremonies and public
events; around 77 percent of the respondents stated
that their social lives had changed as a result of the
fear of contracting Ebola through social gatherings.
The perceived fear caused resistance among the
population that not only complicated containment
measures, but also led to deaths of health workers.
For instance, in September 2014, eight members of
an anti-Ebola campaign team were assaulted and
killed in Wome, a village near N’zérékoré in Guinea.
They were accused by villagers of spreading the
EVD by spraying chlorine in infected communities.
With better community engagement and proactive
awareness raising, people have started to avoid
risky cultural behaviours. Awareness was raised
on the risk of transmitting Ebola through direct,
close contact, body liquids and infected objects,
and risky ceremonies were reduced as much as
possible. Furthermore, the manner in which
traditional initiation ceremonies and rites are
being conducted has started to change (figure 23).
Inter-communal relationships have been
weakened. The relationships between different
communities were weakened by the EVD.
According to the survey, the frequency of visits
to relatives and the relationships between villages
and ethnic groups were negatively affected.
People expressed their regret that they were not
able to visit relatives or attend burials in other
villages. When they attended burials, they could
not carry out traditional ceremonies as they
used to do. Feelings of distrust among people
Figure 23: Changes in the practice of traditional initiation ceremonies
Don’t know 18%
Same 20%
Interrupted 62%
Source: Authors’ estimation.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
53
of different localities and villages are still strong
(60 percent), especially towards those from
localities with high rates of Ebola outbreak.
Early recovery programmes should focus on
strengthening these relationships.
Access to health services has been sharply
reduced. Access to birth control services and
to skilled birth attendants has become more
difficult, according to 63 percent and 53 percent
of the responsdents, respectively (figure 24).
This difficulty is a consequence of the Guinean
Government’s efforts to contain the EVD and
prevent transmission to non-Ebola patients. Fears
spread faster than the virus. Fear and ignorance
engendered the perception that health facilities
were the sources of infection, which made access
difficult. In addition, since EVD symptoms
are similar to the signs of endemic diseases in
the region, the patronage of health facilities is
further complicated. As a result, access to some
hospitals was denied to non-Ebola patients. In
some cases, due to fear of the EVD and to high
fatalities of health workers (90 out of 153 infected
health workers died in Guinea), some medical
clinics have closed down temporarily. Moreover,
medical personnel, including nurses and doctors,
stayed home because they felt unprotected
against patients who may also have EVD. Overall,
the health system was weakened. This will have
a negative impact on the achievement of the
MDGs, especially child and maternal health, as
well as the endemic diseases in the region, such
as malaria and Lassa fever.
Figure 24: Impact of the Ebola virus disease on access to health services
Change in access to
birth control services
Same 26%
Change in access to skilled
birth attendance services
Same 37%
Don’t know 10%
Don’t know 11%
More difficult 63%
Source: Authors’ estimation.
54
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
More difficult 53%
People remain hopeful that life will return to
normal in about a year, but EVD has affected their
expectations about the future. Guineans were
asked about when their lives would return to what
they were like before the EVD. They responded
that they were optimistic about how quickly they
will return to normal life: more than two thirds
(68.8 percent) expected to return to a normal life
within six months, and 87 percent within one year;
and around 13 percent believed that it would take
more than a year for normalcy to return. Very few
people (around 2.2%) were sceptical about life
returning to normal.
Guineans were also asked about how the EVD had
affected their expectations about the future. Ebola
had a negative impact on people’s perception of the
future. For the majority of the respondents, Ebola
diminished their hopes for the future (54%); only 46
percent stated that the future would either remain
as before the EVD or would improve. The social
impact of the EVD is mostly reflected by a general
fear among the population. People expressed fear
for the future of their family, community and for
the whole country (figure 25). At the family and
community levels, this fear is aggravated among
people who lost relatives or community members
or whose livelihoods have been destroyed (figure
16).
Based on past experience with respect to access
to basic service delivery and the way that the
EVD was managed at the onset, people’s overall
confidence in the government regarding its
Figure 25: Fear for the future, at the family and community, and country levels
Family and community level
Don’t know 3%
Country level
Don’t know 4%
Less 2%
No 8%
No 8%
Yes 87%
Yes 88%
Source: Authors’ estimation.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
55
ability to successfully manage the EVD crisis
is very poor; around 72 percent lost confidence
in the government during this EVD period crisis
(figure 26).
The government has an important role to play
in building confidence. How early recovery is
managed is key. Effective management will help
build trust and confidence in the people and boost
their expectations for the future.
Figure 26: Change in people’s trust and confidence
in the government
100%
90%
More 23%
Guineans
commended
the
international
community response to Ebola. The vast majority of
respondents affirmed that the support and response
from the international community enabled a good
and efficient management of the Ebola crisis. More
than 80 percent rated the response excellent. Yet,
some still see room for improvement, especially
on issues2014
relating to
the coordination
of
efforts and
2015
2016
2017
in helping to mitigate the negative impacts of the
EVD at the community level.
80%
70%
60%
50%
Less 72%
40%
30%
20%
10%
Same 5%
0%
Change in Peoples’ trust and
confidece in government
Source: Authors’ estimation.
56
The national government was rated better
than local administrations while international
community ranked highest in responding to EVD.
Guinean authorities’ responses to the EVD were
rated appropriate and the national government was
rated above the local administration: around 48
percent ranked the national government excellent
in its response compared to 44 percent for the
local administration. The local administration
was seen as impenetrable on issues relating
to culture. However, the fact that some local
government members or local chiefs were victim
of Ebola or have lost relatives due to the Ebola has
facilitated awareness raising in some localities. This
boosted local government collaboration on the
implementation of Ebola intervention and control
measures.
4.2 Impact on poverty
147
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
The analysis of the impact of the EVD on poverty
is presented in two stages. The first discusses
Center the Right
impact left
on poverty in the three countries
affected by the disease. The second presents the
results in other countries, including Nigeria,
Mali and Senegal, which have recorded cases of
Ebola in their territory but whose magnitude
is significantly below that recorded in the three
epicentre countries.
Poverty impact in Guinea, Liberia and Sierra
Leone. Due to the political crisis and the disastrous
economic policies over the years in Guinea, and
the decade of civil war in Liberia and Sierra
Leone, more than half of the population of these
countries live below the national poverty line.
The proportion of the poor in the population was
52.9 percent in Sierra Leone (2011), 55.2 percent
in Guinea (2012) and 83.8 percent in Liberia
(2011). However, with the previous economic
outlook, a significant decrease in the poverty rate
in the three countries was expected. Thus, with a
growth elasticity of poverty estimated at -0.74, the
incidence of poverty should be 49.78 percent in
Guinea, against 31.20 percent in Sierra Leone and
63.47 percent in Liberia in 2016.
However, the Ebola epidemic outbreak will
significantly affect the capacity of the countries
to achieve their poverty reduction objectives. The
available evidence indicates that the epidemic
will affect production in strategic sectors such
as agriculture and transportation, as well as the
informal sector where employees are poorest.
Figure 27 shows that the epidemic will have a
significant impact on the incidence of poverty in
the three countries. The magnitude of this impact
depends on the elasticity of poverty with respect to
growth and economic growth rates.
The results for Guinea show that, in 2014, the
poverty rate increased from 2.25 to 2.65 percent
relative to the baseline for the low and high
Ebola scenarios, respectively. Despite the modest
recovery in economic growth that will start in
2015, it remains very weak and insufficient to
reduce poverty. On the contrary, the gap between
the level of poverty with and without Ebola will
continue to grow in 2015 and 2016. In 2015, it will
reach 7 percent in the low Ebola scenario and 7.92
percent in the high Ebola scenario.
The EVD impact on poverty is high in
Sierra Leone. With the appreciable growth rate
in recent years and the bright economic outlook,
poverty had started to decline significantly;
however, this outlook will be greatly compromised
between 2014 and 2016 due to the Ebola epidemic.
Its EVD impact will lead to an increase in the
poverty rate by 13.76 percent in the low Ebola
scenario and 14.11 percent in the high Ebola
scenario in 2015, and by 21.29 percent in the low
Ebola scenario and 21.79 percent in the high Ebola
scenario 2016, relative to the baseline (figure 27).
In examining the estimated results, Liberia
is also the most affected country in terms of
poverty. The poverty situation in the country
prior to the outbreak was already alarming – at
83.3 percent in 2011. In 2014, the poverty rate
is estimated to have been 5.46 percent higher
in the low Ebola scenario and 5.89 percent
higher in the high Ebola scenario scenario,
both relative to the baseline (no Ebola). The
difference in the incidence of poverty between
the low and high Ebola scenarios will continue
to widen in 2015 (17.58 percent) and 2016
(19.2 percent) (figure 27).
Poverty impact in the other countries. The impact
of the Ebola epidemic on poverty is also notable in
the neighbouring countries of Guinea, Liberia and
Sierra Leone. In contrast to Senegal, Côte d’Ivoire,
Guinea Bissau and Mali have experienced serious
crises, which negatively affected economic growth
and exacerbated poverty between 2011 and
2013. Thus, given the economic growth of these
countries, the incidence of poverty could be around
50 percent in Côte d’Ivoire in 2011, and 68 percent
in Guinea Bissau and 45 percent in Mali in 2013.
Although the economies of these countries have
started to recover, showing strong performances,
particularly in Côte d’Ivoire, the Ebola outbreak
could hamper prospects for poverty reduction.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
57
Figure 27: Poverty impact of the Ebola virus disease in Guinea, Liberia and Sierra Leone
Liberia
55
6.75
53
4.5
51
2.25
49
2011
2012
2013 2014
2015 2016
100
0
25
Change in poverty (%)
9
Poverty rate (%)
57
Change in poverty (%)
Poverty rate (%)
Guinea
75
18.75
50
12.5
25
6.25
0
2011
2012
2013 2014
2015 2016
0
60
30
45
22.5
147
Center
30
15
15
7.5
0
2011
2012
2013 2014
2015 2016
Change in poverty (%)
Poverty rate (%)
Sierra Leone
0
Right
left
% change in poverty, Low Ebola scenario
% change in poverty, High Ebola scenario
Poverty without Ebola
Poverty, Low Ebola scenario
Poverty, High Ebola scenario
Source: Authors’ estimation.
The estimated results indicate that, in 2014, in
Côte d’Ivoire, the poverty rate has 147
risen by
Center
between 0.5 (low Ebola scenario) and 0.58 percent
(high Ebola scenario) above what it would have
been without Ebola. These figures could reach
58
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
2.27 percent in 2016 (figure 28). In 2016, in
Guinea
Right Bissau,
left the poverty rate could rise by
about 2.33 percent compared to what it would be
without Ebola, assuming a high level of infection
(figure 29).
Figure 28: Poverty impact of the Ebola virus disease in Benin, Burkina Faso, Côte d’Ivoire and Ghana
4
33.5
3
31
2
28.5
1
2010 2011 2012 2013 2014 2015 2016
0
10
37.5
7.5
25
5
12.5
2.5
0
2010 2011 2012 2013 2014 2015 2016
3.00
51.25
2.25
2016
43.75
0.75
Change in poverty (%)
7.5
Center
1.50
40
0.00
2008 2009 2010 2011 2012 2013 2014 2015 2016
22.5
15
147
47.5
Poverty rate (%)
55
Poverty rate (%)
30
Ghana
Change in poverty (%)
Côte d’Ivoire
0
26
1.5
24
1.125
Right
left
22
0.750
20
0.375
18
2012
2013
2014
2015
2016
Change in poverty (%)
26
50
Change in poverty (%)
36
Poverty rate (%)
Burkina Faso
Change in poverty (%)
Poverty rate (%)
Benin
0
% change in poverty, Low Ebola scenario
% change in poverty, High Ebola scenario
Poverty without Ebola
Poverty, Low Ebola scenario
Poverty, High Ebola scenario
0
147
Center
Right
left
Source: Authors’ estimation.
147
Center
Right
left
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
59
3 2014
Figure 29: Poverty impact of the Ebola virus disease in The Gambia, Guinea Bissau, Mali and Niger
10
45
7.5
30
5
15
2.5
0
0
2010 2011 2012 2013 2014 2015 2016
60
8
45
6
30
4
15
2
0
2010 2011 2012 2013 2014 2015 2016
30
7.5
2015 2016
4.5
147
Center
42.5
3.0
38.75
1.5
60
20
65
15
Right
left
60
10
55
5
50
2010 2011 2012 2013 2014 2015 2016
% change in poverty, Low Ebola scenario
% change in poverty, High Ebola scenario
Poverty without Ebola
Poverty, Low Ebola scenario
Poverty, High Ebola scenario
0
147
Source: Authors’ estimation.
147
70
Center
Right
left
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Center
Right
left
0
Change in poverty (%)
46.25
Poverty rate (%)
6.0
Change in poverty (%)
15
50
35
0.0
2010 2011 2012 2013 2014 2015 2016
22.5
0
Niger
Change in poverty (%)
Poverty rate (%)
Mali
Change in poverty (%)
60
Poverty rate (%)
Guinea Bissau
Change in poverty (%)
Poverty rate (%)
The Gambia
Figure 30: Poverty impact of the Ebola virus disease in Nigeria, Senegal and Togo
Senegal
1.125
42.5
0.75
38.75
0.375
Poverty rate (%)
46.25
50
6.0
46.25
4.5
42.5
3.0
38.75
1.5
Poverty rate (%)
1.5
Change in poverty (%)
50
35
0
2010 2011 2012 2013 2014 2015 2016
35
2010 2011 2012 2013 2014 2015 2016
Change in poverty (%)
Nigeria
0.0
60.5
45
0.75
22.5
Center
147
59.0
30
0.50
15
57.5
15
0.25
7.5
56.0
0
00.00
2010
2011
2012
2013
2014
2015
2016
2011 2012 2013 2014 2015 2016
50
6.0
46.25
4.5
Right
42.5
left
% change in poverty, Low Ebola scenario
3.0
% change in poverty, High Ebola scenario
38.75
Poverty without Ebola
1.5
Change in poverty (%)
1.00
30
Poverty rate (%)
62.0
60
Change
Changeininpoverty
poverty(%)
(%)
Povertyrate
rate(%)
(%)
Poverty
Togo
Poverty, Low Ebola scenario
35
0.0
Poverty, High Ebola scenario
2010 2011 2012 2013 2014 2015 2016
Source: Authors’ estimation.
Finally, among the three countries that have
experienced political crises at the beginning
of
Center
147
Center
147
the decade, Mali will be one of the most severely
affected by changes in the incidence of poverty. In
2014, the proportion of poor in Mali’s population
could increase by 1.72 to 2.07 percent compared
to what would be observed without Ebola. In
2016, these figures will rise to 4.12 percent in the
low Ebola scenario and 4.88 percent in the high
Ebola scenario.
For Senegal, in 2014, the proportion of people
living
belowleft the national poverty line could
Right
Right
left
increase by 1.4 to 1.8 percent. In 2016, these
figures could reach 3.59 percent in the low Ebola
scenario and 4.92 percent in the high Ebola
scenario (figure 30).
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
61
4.3 Food security impact of the Ebola
virus disease
The food security impact is evaluated with the
prevalence of undernourishment measured by the
proportion of the population estimated to be at
risk of caloric inadequacy. Figure 31 presents the
prevalence rate of undernourishment in ECOWAS
countries in 2012 and its growth rate between 1992
and 2012.
The prevalence of undernourishment is generally
decreasing in the West African region. The most
significant progress has been observed in Ghana,
where the prevalence of undernourishment
decreased on average by around 10 percent per
year between 1992 and 2012. In the same period,
there were declines of around 5 percent per
year in Niger, Mali and Benin against an annual
fall of 4.22 percent in Nigeria. Côte d’Ivoire
and Burkina Faso are the only countries where
there has been an increase in the prevalence
of undernourishment: from 1992 to 2012, it
increased by an average of 2.38 percent in Côte
d’Ivoire and 0.65 percent in Burkina Faso.
Figure 31: The prevalence of undernourishment in West African countries, 1990–2012
Undernourishment Prevalence (% of total population in 2012)
Annual average growth rate (%)
35.00
4
26.25
0
17.50
-4
8.75
-8
0
Benin
Burkina Cote
Faso d'Ivoire
Cape
Verde
Ghana
Guinea Gambia Guinea
Bissau
Liberia
Mali
Niger
Nigeria Senegal Sierra
Leone
Togo
-12
Note for the legend: BEN – Benin, BFA – Burkina Faso, CIV – Côte
d’Ivoire, CPV
– Cape Verde, Right
GHA – Ghana, GIN
Center
left– Guinea, GMB – The Gambia, GNB – Guinea Bissau,
147
LBR – Liberia, MLI – Mali, NER – Niger, NGA – Nigeria, SEN – Senegal, SLE – Sierra Leone, and TGO – Togo.
Source: Authors’ estimates from FAO food security indicators.
62
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Despite progress, the situation remains
serious in most countries, particularly Liberia
and Sierra Leone, where undernourishment
affected 31.4 percent and 28.8 percent of
the population, respectively, in 2012. The
undernourishment prevalence rate was above
20 percent in Burkina Faso, Côte d’Ivoire and
Senegal in the same year.
Given the impact of the EVD on sectoral output,
especially agricultural outputs, and overall
economic growth, food insecurity is expected to
be deeply affected in a negative way, particularly
in the three epicentre countries. The results of
the econometric estimation indicate that there is
a negative and significant statistical relationship
between the undernourishment prevalence rate
and the rate of economic growth (see table 13
and figures 32-35).
Table 13: Food security panel data model results
Log of undernoutrition
Coefficient
Std. Err.
t
P>|t|
Log of GDP per capita
-.3901932
.0317976
-12.27
0.000
Log of labour
1.588818
.4351351
3.65
Trade openness ratio
.0015213
.0009085
Burkina Faso
.1389659
Cote d’Ivoire
Cape Verde
[95% Confidence - Interval]
-.4527775
-.3276089
0.000
.7323824
2.445254
1.67
0.095
-.0002669
.0033094
.0958749
1.45
0.148
-.0497357
.3276674
.5394639
.0850728
6.34
0.000
.3720232
.7069047
.4678934
.0940117
4.98
0.000
.2828591
.6529278
Ghana
-.0304974
.0742438
-0.41
0.682
-.1766246
.1156298
Guinea
.2174014
.0690204
3.15
0.002
.081555
.3532478
Gambia
.2364316
.0736268
3.21
0.001
.0915189
.3813442
Guinea Bissau
.1271463
.0702
1.81
0.071
-.0110218
.2653144
Liberia
.5622215
.1298036
4.33
0.000
.3067412
.8177018
Mali
.5081266
.1369746
3.71
0.000
.2385324
.7777209
.673469
Niger
.477993
.0993169
4.81
0.000
.2825169
Nigeria
.0801303
.1357143
0.59
0.555
-.1869834
.347244
Senegal
.4327793
.0746103
5.80
0.000
.2859309
.5796277
Sierra Leone
.9007052
.0846185
10.64
0.000
.7341586
1.067252
Togo
.1905544
.0809944
2.35
0.019
.0311406
.3499681
Constant
-1.833065
1.915975
-0.96
0.340
-5.604099
1.937969
Number of obs = 307
F( 17, 289) = 55.25
Prob > F = 0.0000
R-squared = 0.7647
Adjusted R-squared = 0.7509
Root MSE = .21909
Source: Authors’ estimation
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
63
Figure 32: Impact of the Ebola virus disease on food security in the main epicentre countries
Guinea
Liberia
13.85
13.30
12.75
12.20
34
Undernourishment rate (%)
34
Undernourishment rate (%)
Undernourishment rate (%)
14.40
Sierra Leone
32.5
31
29.5
28
2013 2014 2015 2016
32.75
31.5
30.25
2013 2014 2015 2016
Trend
Low EVD
29
2013 2014 2015 2016
High EVD
Source: Authors’ estimation.
147
Right the left
Liberia was the second most affected country
inCenter Although
results of the estimation show that
the three countries as of 28 December 2014. In the
the impact of the disease on food security is lower
case of a low level of infection, the prevalence of
in Sierra Leone and Guinea, these two countries
undernourishment could increase by 2.82 percent in
will not be spared from the worsening of food
2014, compared to the trend observed between 1992
insecurity. In Sierra Leone, the epidemic will result
and 2012. In the low Ebola scenario, this increase
in increased food insecurity, with the prevalence of
could reach 4.17 percent in 2015 and 5.27 percent in
undernourishment rising with the prevalence of
2016. In the high Ebola scenario, the prevalence of
infection. Assuming a moderate level of infection,
undernourishment increases by 5.80 percent in 2016.
the prevalence of undernourishment will increase by
These results are consistent with observations that
1.30 percent in 2014 compared to the trend observed
are visible on the ground. In fact, in addition to the
between 1992 and 2012; in the high scenario, this
worsening of income poverty that reduces capacity
increase is by 1.39 percent. These rates will be 4.03
to access food, the epidemic has left its mark on the
percent and 4.12 percent higher, for low and high
country’s agricultural production and marketing
scenarios, respectively, in 2016.
systems. The Global Information and Early Warning
Guinea will be less affected than Liberia and
System on Food and Agriculture (GIEWS, 2014)
Sierra Leone. The increase in the prevalence
observed that the areas with high incidences of the
of undernourishment due to the EVD will be
EVD are among the most productive regions of
0.49 percent in the low scenario and 0.57 percent
Liberia, and that the outbreak of the EVD, together
in the high scenario in 2014, compared to the
with the restrictions on the movement of people and
trend observed in recent years. These changes
the supply of labour, has led to serious concerns with
in food insecurity could reach 1.50 percent and
respect to food production.
64
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
1.72 percent, in 2016. However, despite the limited
impact on the food insecurity indicator measured
at the national level, the most affected regions
may experience more difficulties in access to
food products of sufficient quality and quantities.
Indeed, Guinée Forestière, where the epidemic
has been mostly concentrated, experienced a
greater disruption of agricultural production
and agricultural marketing systems. As a result,
employment and income could be more greatly
reduced. Furthermore, Moyenne-Guinée, which
is a fruit, vegetable and potato production area,
has faced the closure of its border with Senegal
in particular. Indeed, much of the production
of this part of the country is traditionally exported
to Senegal; the border closure has reduced the
export opportunities.
Figure 33: Impact of the Ebola virus disease on food security in Benin, Burkina Faso, Côte d’Ivoire and Ghana
Benin
Burkina Faso
30
Undernourishment rate (%)
Undernourishment rate (%)
9
8.5
8
7.5
7
2013
2014
2015
2013
High EVD
2014
2015
2016
2015
2016
Ghana
5.0
24.75
23.50
147
Center
22.25
2013
2014
2015
Undernourishment rate (%)
Undernourishment rate (%)
27
Low EVD
26.00
21.00
28
26
2016
Trend
Côte d’Ivoire
29
2.0
Right
left
0
-1.0
2016
Trend
3.5
Low EVD
2013
2014
High EVD
Source: Authors’ estimation.
147
Center
Right
left
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
65
Figure 34: Impact of the Ebola virus disease on food security in The Gambia, Guinea Bissau, Mali and Niger
The Gambia
Guinea Bissau
40
Undernourishment rate (%)
Undernourishment rate (%)
14.5
14.0
13.5
13.0
12.5
2013
2014
2015
2013
High EVD
2014
2015
2016
2015
2016
Niger
12
7.5
5.0
147
Center
2.5
2013
2014
2015
Undernourishment rate (%)
Undernourishment rate (%)
10
Low EVD
10.0
0.0
20
0
2016
Trend
Mali
30
6
Right
Low EVD
left
3
0
2016
Trend
9
2013
2014
High EVD
Source: Authors’ estimation.
When analysing the other West African countries,
it emerges that the magnitude of the impact of the
epidemic on food security will be severe
147in the
Center
bordering countries of Guinea, Liberia and Sierra
Leone. Being the most vulnerable economically
among these countries, Guinea Bissau will be most
affected by food insecurity. Indeed, the level of
undernourishment is estimated to have increased
by 3.64 to 3.68 percent in 2014, compared to the
observed trend; this increase could reach about 11
percent in 2016.. The country has just ended a crisis
that affected the entire agricultural production and
food supply systems. This was further compounded
by the closure of the southern and eastern borders to
Guinea on 12 August 2014.
66
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Despite the weight of its economy and the strength of
the economic recovery that followed the post-election
crisisRight
of 2010-2011,
Côte d’Ivoire is the second
left
country where the food security impact has been
estimated to be the second greatest. The estimated
increase in food insecurity is 1.14-1.39 percent in 2014,
compared to the trend observed between 1992 and
2012. However, the situation could improve in 2016,
with a lower increase in food insecurity of around
0.45 percent in the case of a high level of infection.
The case of Côte d’Ivoire could be explained by
its being the largest economy within WAEMU,
accounting about 50 percent of intra-WAEMU trade
(Philippe and Nayo, 2011). In addition, as the only
country in the region that shares borders with two of
Figure 35: Impact of the Ebola virus disease on food security in Nigeria, Senegal and Togo
Nigeria
Senegal
17.4
Undernourishment rate (%)
Undernourishment rate (%)
6.4
6.1
5.8
5.5
5.2
2013
2014
2015
2013
2014
2015
2016
2014
2015
2016
High EVD
30
16.25
15.5
147
14.75
2013
2014
2015
Center
Undernourishment rate (%)
Undernourishment rate (%)
16.2
Low EVD
17
14
16.6
15.8
2016
Trend
Togo
17.0
28
Right
Low EVD
left
27
26
2016
Trend
29
2013
High EVD
Source: Authors’ estimation.
the three affected countries, and given the level of the
(Mali). In 2016, assuming a high Ebola scenario level,
informal economy in the region, the negative impact
these figures rise to 1.12 percent (Senegal) and 1.13
on informal trade and production activities with
However, the most severely affected
147 CôteCenter percent
Right(Mali).left
d’Ivoire could be large. Due to the higher likelihood
by food insecurity could be the border areas of these
of contamination by disease, Côte d’Ivoire closed its
countries, which depend more on cross-border trade.
borders with Liberia and Guinea on 22 August 2014.
For the remaining countries, the impact is almost
This is all the more worrying since Côte d’Ivoire,
marginal. Only The Gambia recorded an increase
as mentioned above, is one of the most important
in prevalence of undernourishment, which could
countries in the sub-region due to its weight in
reach 1 percent in 2016. In Nigeria, Ghana and
regional trade and due to the potential ripple effect
Togo, for example, food insecurity increased by 0.20
on the rest of the West African economies.
percent, 0.096 percent and 0.36 percent, respectively,
In 2014, Senegal and Mali recorded a slight increase
in 2016, assuming a high Ebola scenario.
in the prevalence of undernourishment from 0.52
to 0.19 percent (Senegal) and 0.37 to 0.55 percent
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
67
UNDP donated hygiene kits to civil society leaders in Nzérékoré, Guinea for community mobilization and engagement. Photo by UNDP Guinea
Conclusions and policy
recommendations
5.Conclusions and policy
recommendations
This report is unique.
Unlike previous studies that focused on the
three epicentre countries and provided aggregate
estimates, this is the first report to undertake an
assessment of the EVD for each of the 15 West
African countries. It is also the first to assess the
socio-economic impacts of the EVD on poverty
incidence and food security at the country level.
In addition, the estimation approach, which allows
for consistency checks, is also different from that
of other studies.
The EVD outbreak in West Africa is highly
intense, virulent, complex and challenging. As of
28 December 2014, there were 20,081 confirmed
cases and 7,842 deaths as a result of this disease.
The basic reproductive number of 2.5 is the highest
ever in the history of EVD globally. Its dimension
is more complicated by health workers becoming
infected and by the occurrence of a different strain
of EVD in the DRC (WHO, 2014). It also impacts
on the human rights situation in the region, with
significant negative effects on social, cultural, and
economic rights of affected populations.
A combination of factors – ignorance, lack of
preparedness of the health system, and fear
and distrust – contributed to the rapid spread
of EVD. First, all three countries have recently
emerged from civil conflicts or political instability
that resulted in countless deaths, economic crises,
and a severe deterioration in physical infrastructure
and social conditions. Second, health professionals
are unfamiliar with the disease, particularly since
the symptoms resemble those of other diseases
that are endemic in the region. Third, the paucity
of knowledge on the disease, combined with
the fear produced by the epidemic, delayed the
implementation of simple interventions to prevent
deaths. Fourth, the health systems in the region
were unprepared for Ebola at the outset of the
epidemic. They lacked sufficient amounts of all
that is required to contain the epidemic: drugs,
ambulances, facilities and trained health personnel,
and medical facilities are inequitably distributed
between rural and urban areas, thereby limiting
access to basic health services in remote areas. Fifth,
the fear of being quarantined or being infected at
the health centres has discouraged both testing and
treatment. And finally, the long-standing cultural
practices that people were understandably reluctant
to abandon also helped spread the infection.
Women are heavily affected by the EVD. In
Guinea, the epidemic affects more women than
men, a disparity that could be explained by their
role within the family as the primary caregivers to
the sick, which rendered them more vulnerable. The
regional disparity is more pronounced: in Gueckédou
62 percent of the infected people are women and in
Télémilé, 74 percent. When Ebola struck Liberia
in August, the proportion of births supervised by a
health professional dropped from 52 to 38 percent,
and the number of women giving birth in hospitals
and health clinics dropped by 30 percent in Sierra
Leone, in addition to other impacts on women.
Some important lessons learned can be drawn
from the EVD experience in West Africa
and should be factored into its containment,
recovery and preventive measures.
First, addressing public health crisis requires
trained specialists with a clear understanding of
the associated protocols. Second, culture matters
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
69
in addressing emergencies – tackling risky
traditional behaviours calls for better appreciation
of the context among the traditional and religious
leaders, and the community. Third, health is as
important as the economy – without a healthy
population, promoting a rapid and sustained
economic growth is difficult. Fourth, addressing
an epidemic like EVD underscores the important
of trust between the people and their government.
Fifth, leadership matters – political and community
leaders must take the lead as champions in dealing
with the crisis. Sixth, since Guinea, Liberia and
Sierra Leone are heavily affected by the EVD,
the economic and socio-economic impacts on
unaffected countries are potentially high. And
finally, the impact of stigmatization and risk
aversion behaviours could be pronounced on the
national and regional economy.
Most West African countries are vulnerable to
Ebola outbreak.
The health systems and development outcomes
of most of the countries in the region are
similar to those in the three epicentre countries.
They are therefore not immune from EVD
outbreak and thus urgent that they strengthen
their health systems, institutionalize preventive
measures, and invest heavily in disaster risk
reduction and management system. This calls
for commitment to the implementation of Abuja
Declaration – increasing budgetary allocation to
the health sector.
The toll on economic activity is huge.
One year after the first Ebola case was detected in
Guinea, the disease still remained uncontained
and continues to decimate the populations of
Guinea and Sierra Leone as of 11 February 2015.
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SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
The economic situation in these countries is
rapidly deteriorating, and the trend is expected to
continue until the disease is finally contained and
recovery plan rapidly scaled up. Even containing
the disease in the epicentre countries does not
render other West African countries immune
from the socio-economic impact; they have
started to feel the impact. Even under the low
Ebola scenario, the estimated loss in GDP for
the 15 countries in the region is US$1.8 billion in
2014, which is expected to rise to US$3.4 billion
in 2015 and US$4.7 billion in 2017. The EVD will
reduce GDP growth by 0.8 percentage points on
average between 2015 and 2017. A loss in around
1.2 percent of the region’s GDP on average poses a
serious challenge. The region’s per capita income
is expected to fall by US$18.00 per year during
the period. Both in terms of GDP and per capita
incomes, this result tends to support the need
to give priority attention to regional preventive
measures and to highly proactive recovery
measures in the epicentre countries. The loss of a
per capita income of US$18.00 in a region where
most of population live below the poverty line
of US$1.25 per day has serious consequences on
the people.
The impact on human development is alarming.
Many women are now giving birth outside the
modern health facilities, thereby making them
vulnerable during complex situations. Children in
the epicentre countries were pulled out of schools;
in Nigeria, the opening of schools was also delayed.
The increasing wave of orphans associated with the
EVD is also a concern. When they are no longer be
cared for by the community, the long-term human
development impact could be more complex
to address.
The Ebola virus disease’s impact on poverty is
strong in West Africa.
The expected impact of the accelerated growth
on poverty reduction in the years ahead has been
wiped out. The impact of the EVD on poverty
shows a very strong increase in poverty over the
medium-term period in the region, which is much
more pronounced in the epicentre countries. Given
a low Ebola scenario, poverty is estimated to rise
by between 2.3 and 2.6 percent in 2014, and could
further rise by 7.1 percent in 2015. The situation is
worse in Sierra Leone (13.7 percent in 2014 to 21.8
percent in 2016) and Liberia (17.5 percent in 2015
to 19.2 percent in 2016). The poverty rate in Mali
has also been estimated to increase by between
1.7 and 4.1 percent (low Ebola scenario) during
2014-2016. It also affects the poverty situation of
the unaffected countries in the region: 0.5 percent
(Côte d’Ivoire) and 1.4 percent (Senegal) in 2014.
The impact on food security could be very
significant.
The impact of the EVD on undernourishment
raises serious concerns over whether the SDGs
in the epicentre countries and their neighbours
will be smoothly rolled out. Among the epicentre
countries, the impact on undernourishment could
be higher in Liberia than in Guinea and Sierra
Leone. The undernourishment rate will range
between a 2.28 and 4.17 percent increase between
2014 and 2016 compared to between 0.5 and 1.5
percent in Guinea, and between 1.3 and 4.0 percent
in Sierra Leone. The situation is similar in Guinea
Bissau, Côte d’Ivoire and Mali. This calls for a very
strong social protection mechanisms for heavily
affected people, especially pregnant women and
children. The governments, UN agencies, donors,
NGOs and other stakeholders should focus more
attention on this issue.
The anthropological findings confirm
the heavy toll of the Ebola virus disease
on household economic activities, living
conditions and social relations.
The closure of boarders, the lull in private
businesses, fear and stigmatization impede
households’ economic activities. Many people
changed their consumption habits and had to
eat less than they were doing before the EVD
outbreak. Although most people still think family
and societal ties remain strong, there is evidence
that the EVD is eroding age-long communal
behaviours including reduced attendance at
ceremonies, adjustment in burial rights, and
declined care of family and community members.
Feelings of distrust between communities, and
between citizens and government are still strong.
The health system had been weakened in terms
of access to health services including non-Ebolarelated services such as family planning, pre- and
post-natal services, antiretroviral therapies and
treatment of endemic diseases in the region –
malaria and cholera. Although most Guineans are
optimistic that they will return to normal life in
six months to one year, many of them expressed
a fear for the future of their family, community
and for the whole country. The recovery and
future preventive measures must take cognizance
of people’s feelings and expectations. Effective
management of the disease and recovery process
will help build trust and confidence in the people
and also boost expectations for the future.
The UN agencies’ programmatic engagements
in the affected countries are challenged.
The spread of Ebola challenges the different
UN agencies with extremely difficult trade-offs
between the overriding need to stop the epidemic
as soon as possible, the urgent need to prevent
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
71
affected communities from sinking further into
poverty, and the obligation to continue efforts
towards achieving long-term development.
The international community must act
very swiftly to avoid a ‘double failure’
phenomenon – the perception of national
stakeholders about how the donor community
responds to the crisis.
The United Nations still remains a respected
partner in West Africa, but much more is
needed in upholding the trust gained from the
governments of these countries. The UN agencies
must be able to rapidly adapt to new epidemic
contexts. This includes improving the upward
flow of information related to experiences in other
countries, mobilizing partnerships around the
containment and early recovery of governments,
strengthening coordination among the UN
agencies and helping governments to effectively
coordinate the global support, among others. UN
agencies must also foster information sharing,
namely, by making recommendations that will be
followed by all communities and by all NGOs in
the field. More coordinated actions at the local,
national and international levels are a prerequisite
for containing the epidemic.
Actions must be strengthened to improve
communicating information to communities
regarding the nature of the disease, and social and
health-related ways to address it. It is important
to consider any incentive that would encourage
populations to take charge of their situation,
in particular, by declaring and preventing the
disease. This is needed to avoid ‘double failure’
phenomenon. Given that the international
responses were slow in coming right from the
72
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
beginning, they need to be very swift and flexible
in adapting to the prevailing circumstances in each
country. Support must be directed to where it is
mostly needed. All efforts should be dedicated for
an effective early recovery to further boost people’s
confidence in the relevance of the United Nations
and its unparalleled support in times of need.
It is essential to draw on lessons learned from
other countries’ experiences.
The experiences of Nigeria and Senegal have
shown that a decentralized health management
system can play a very important role in
forestalling a repeat of this development in the
future. The rapid response witnessed in Nigeria,
for example, can be explained by decentralized
development efforts where local authorities had
the authority to act without a green light from the
central government in order to enforce quarantine
and other containment measures. UN agencies
could also promote better governance of health
systems by working toward the establishment of
decentralized procedures, which proved effective
in containing the Ebola outbreaks in Nigeria and
Senegal in 2014.
But learning from other countries’ experience
is not as straightforward as it might first appear.
Certain country-specific factors, both institutional
and cultural, are difficult to replicate, and
addressing fear and beliefs is not an easy task.
Accordingly, the government can take fast action
by organizing awareness campaigns and promoting
the critical role of communities in addressing
difficult-to-change risky practices.
A UN women beneficiary from the Cross -Border Trade Programme struggling with her business sales in Liberia. Photo by: UN women, Winston P.Daryoue
Improved regional and
cooperation is important.
international
The experience over the past year in the three
epicentre countries has shown that a single
country cannot successfully manage the crisis on
its own. This unprecedented situation requires
exceptional regional solidarity, collaboration
and collective actions to save lives, rekindle lost
livelihoods, and safeguard peace and security
of the region. This is not only important for
developing priority actions to improve health
systems and ensure access to basic health
services in remote areas, but also for helping
these countries to address vulnerability and
build resilience to shocks. It is essential for each
country to play a role in resolving the crisis.
The Mano River Union and ECOWAS have
a very strong role to play.
The MRU must continuously mobilize highly
effective logistics, operational capability and
unparalleled solidarity for early recovery actions
and preventive mechanisms (including early
warning system), and for building community
resilience such as enhanced capacity to manage
emergencies. The MRU and ECOWAS should
capitalize on the opportunity provided by the
Ebola crisis to address the region’s institutional
and economic fragility. The Ebola experience
highlights the urgent need to rebuilding medical
institutions and infrastructure for the next
generation. The region must take advantage of
the enhanced momentum from development
partners and the international community to
maximize their support in strengthening the
health, education, economic and governance
institutions to overcome perennial and emerging
development challenges.
It is crucial to prepare early warning
mechanisms in non-affected countries.
The health system management in Guinea, Liberia
and Sierra Leone is similar to most other African
countries. To this end, the Regional UNDG-WCA
calls on ECOWAS and the African Union to
institutionalize highly preventive mechanisms to
forestall a repeat of the current situation in the
near future. Current efforts of the African Union
in mobilizing solidarity actions from other African
countries and partnerships with the private sector
should be strengthened.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
73
The United Nations should work with
ECOWAS to ensure that closed borders
are reopened and that international
stigmatization is addressed.
Ebola Solidarity Fund must be managed
efficiently and should be transformed
into a post-Ebola tropical disease control
endowment fund.
Early recovery cannot take root if borders are
still closed. The closure of borders may have hurt
growth-led investments and disrupted the regional
integration process necessary for growth and
development. Efforts to ensure that borders are
opened should be giving a priority by Mano River
Union, ECOWAS as well as UNDP and relevant
UN agencies.
The Ebola Solidarity Fund established by
ECOWAS and the ‘Stop Ebola’ Fund established
by the African Union Commission are epochal.
Efficient utilization of these resources is critical.
Transparent and accountable use of these resources
will attract additional funds. This initiative should
not end with Ebola; it should be transformed into
endowment funds for tropical disease control.
The private sector, the United Nations and other
international organizations should work with
pan-African institutions to make this a reality.
There is an urgent need to move from strategy
to concrete actions in the establishment
of the regional Centre for Disease Control
and Prevention.
Both ECOWAS and the African Union have
initiated the process of establishing these centres
at the regional and continental levels. Translating
these declarations into functioning centres is
key. Strategic actions (short-, medium- and
long-term actions) required to make these fully
operational are urgently needed. Mobilizing
financial resources, expertise, technical support
and partnership is ineluctable. In addition, to
avoid unhealthy competition, the activities of the
regional and continental disease centres must be
complementary. The ECOWAS Centre has been
mandated to focus on the conduct life-saving
research on priority health problems in Africa
and to serve as a platform to share knowledge
and build capacity in responding to public health
emergencies and threats. The African Centre
should focus on complementary and high-level
mandates for synergy and unity of purpose.
Medium- and long-term targets must be set for
these centres and mechanisms established for
monitoring performance.
74
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Widespread vulnerability calls for
implementation of effective safety nets.
the
Poverty and food insecurity have increased
and have rendered the population vulnerable.
Children, who are key actors in development,
will be the most affected, partly due to the loss of
parents from the EVD. Women are also especially
impacted by the epidemic because of their
central role in caregiving and in production and
trade activities. In addition, the non-agricultural
self-employed are also particularly impacted
and suffer from high unemployment. Immediate
actions could be undertaken to protect these
vulnerable populations through effective
safety nets that supply resources and activities.
However, in the specific context of the EVD
epidemic, it is important to properly guide the
population to ensure that very strict hygiene
rules are observed and that contact between
individuals is limited.
Social safety nets have the advantage of both
alleviating suffering and reassuring the population
by responding to their needs, thereby helping
people regain confidence in the government and
responding to the crisis. Safety nets could also
take the form of targeted actions in certain activity
sectors that are more predominantly occupied by
women and rural dwellers, especially farming,
trading and small-scale businesses.
An effective social protection mechanism for
women and orphans strongly affected by the
Ebola virus disease is urgently needed.
The safety net mechanism should be decentralized
so that discharge packages for survivors and
compensation for deceased families and orphans
are promptly paid. In addition, governments and
UN agencies should design a comprehensive
and robust programme targeting EVD orphans.
Institutional mechanisms must be provided so that
women in agriculture and the informal sector can
access financial services to protect their livelihoods
and food security.
A strong focus on gender and children in Ebola
virus disease recovery plans is important.
The lessons learned from this epidemic need to
be analysed through a gender lens in order to
translate them into adequate medical and social
responses to meet the specific and differentiated
needs of women and men. Children are not
only heavily and directly affected; some of them
have also become orphans. It is important to
ensure that children are not excluded from the
recovery process. Specific attention should be
given to orphans in the recovery interventions.
Re-establishing livelihoods and kinship ties that
were functioning before Ebola is also critical to
strengthening the resilience of communities.
It is important to prepare schools for the Ebola
virus disease management and regain lost
learning hours.
The total number of learning hours lost to school
closures is extremely high. Programmes must
be put in place to compensate for this loss. The
reopening of schools should be complemented with
back-to-school programmes that focus on teacher
training on school safety, hygiene education and
school sanitation as well as psychosocial care.
Fear and ignorance must be overcome through
better communication.
The psychological factors, including fear, must
be addressed through better communication
and restored trust between populations and care
organizations. Strategic and consultative awareness
creation is needed to address risky practices such
as burial rights. In this respect, traditional and
religious leaders have a significant role to play.
They must first understand the need for changes in
attitudes and risky behaviours. This also requires
improved coordination between non-governmental
organizations (NGOs), traditional and religious
leaders, and government agencies.
Community engagement is a key factor
that has contributed to the success of the
control measures.
Implementation of early and medium-term
recovery must leverage community engagement.
The support must be integrated into positive
cultural values of society. To this end, traditional
and religious leaders must be effectively consulted
for such interventions to be owned, accepted and
successful. Community involvement is an ethical
imperative.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
75
Priority should be given to drawing on
the lessons learned from EVD response to
upgrade the quality of the health system across
the region.
Leveraging the enhanced capacity of the affected
countries to deliver improved health services
across the various endemic diseases affecting the
region is key. The Sierra Leone experience shows
how laboratory capacity could be strengthened
to deliver quality services through better clinical
management services. In less than six months, 11
operating laboratories were equipped and staffed to
process 700 samples per day. The government-run
Hastings Ebola Treatment Centre where six out of
every ten treated patients fully recovered is a very
good example. The rapid improvement of data
quality and reporting mechanism that supported
control measures is also commendable. This
capacity should not be allowed to erode; it should
be integrated into the health development systems
in these countries. Even when the outbreak is
finally contained, the EVD services and facilities
should be dedicated to the region’s endemic
diseases such as malaria, cholera and Lassa fever.
These diseases should be fought with the same
vigour and commitment. It is evident, however,
that the international capacity to deal with large
and geographically dispersed epidemic is limited.
This calls for a scaled global preparedness and an
urgent need for an Ebola vaccine.
It is imperative for governments to lead the
recovery process. Governments and local actors
have a significant role to play in the recovery
process. To ensure sustainability, ownership
and capacity strengthening, the international
76
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
organizations should work with them to mobilize
resources for this process. They should be
supported to effectively coordinate and lead the
implementation of the recovery programme.
The Ebola heroes should be acknowledged and
appreciated. Four sets of heroes emerge from
the fight against Ebola: the Ebola survivors who
fought the disease with courage; the international
organizations that persistently stayed with the
countries when others fled (e.g. the UN agencies,
MSF and Red Cross/Red Crescent); the donors that
made financial contributions to the containment
of the pandemic (e.g. USA, UK, Germany, World
Bank and France); and countries that contributed
health personnel (e.g. Cuba, China, Nigeria,
Ethiopia and Uganda). The UN, the African Union,
ECOWAS and the three epicentre countries should
duly appreciate these actors. These heroes should
also support the recovery process in the heavily
affected countries and help to institutionalize
preventive measures in the unaffected countries in
the region.
After being kept closed for three months due to the Ebola outbreak, schools across Guinea reopened on 19 January 2015. Photo by UNMEER/Martine Perret
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
77
The Western Area Emergency Response Centre in Freetown established to control the rate of infection and stop the spread of the disease in the region. Photo by UNMEER/Martine Perret
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WHO. 2015b. “Situation summary by sex and age group”, http://apps.who.int/gho/data/view.ebola-sitrep.
ebola-summary-age-sex-20150107?lang=en
World Bank. 2014. “The Economic Impact of the 2014 Ebola Epidemic: Short- and Medium-Term
Estimates for West Africa”. Washington, D.C.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
81
A woman in a soap cooperative in Kankan, Guinea. Photo by UNDP/Anne Kennedy
Annexes
Annexes
Annex 1. The non-linear two-stage least square techniques
This model uses non-linear two stage least square techniques. It uses Bloom and Mahal’s (1995) approach
by building a model that captures the potential effect of the Ebola virus disease (EVD) on economic
production. The model is specified as follows:
j
Yit =α + γ i1 Y i(t–1) + γ i 2 Y i (t–2) + β i X it + δZ it. + ε it ( 1)
Where:
I equals 1 to 15, representing the 15 West African countries
T
represents the time period
γi is a vector of parameters
Yit is the real gross domestic product per capita (GDP)
α is a constant
βi is a vector of parameters
Xi is a set of explanatory economic variables that determine Yit
• the total labour force
• education
• openness
• current account balance as a percentage of GDP
δ
Pjit
is the coefficient of the variable that captures the influence of Ebola
is the probability for a country to detect an Ebola case under scenario j (j= no Ebola, Low, High).31
It represents the probability of having an EVD case in the next 30 days under either a no Ebola
scenario, a low or a high Ebola scenario.
The estimation of equation (1) allows to define the coefficients of GDP per capita for each country. Using
forecast values of the explanatory variables, one can forecast the level of production per capita with and
without EVD.
31
There is a dummy variable, which is 1 for Benin, Burkina-Faso, Cape Verde, Niger and Togo; and 0 otherwise. These five countries have not been assigned probabilities for Ebola scenario in the World Bank
report. We have assumed that they have the same probability distribution as similar countries. For Benin, Burkina Faso and Togo, we have used the probability distribution of Côte d’Ivoire. For Cabo Verde and
Niger, the probabilities for Guinea-Bissau and Senegal have been used respectively.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
83
Annex 2. Methodology of the EVD Probability estimation
Using the probabilities of seeded cases in the next 30 days from the World Bank report on the economic
impact of the 2014 Ebola epidemic, this report simulates probabilities of having Ebola cases under low
and high Ebola scenarios for each of the 15 West African countries.
We assume that each country began to implement safety measures to prevent the transmission of Ebola
just after the first seeded case occurs in this country or in neighbourhood countries. However, given the
speed of spreading of Ebola, number of new cases may rise and reach a peak before decreasing. Hence,
the EVD prevalence probability has increased from the date of the first case to the date the peak was
reached. After the peak date, the probability of any new seeded cases is a decreasing function of the
initial measured probability and the efficiency of the intervention measures set up in the country.
In each country, there were different specific and even hidden factors, which contributed to the apparition
of Ebola; i.e. the actual Ebola events probably result from different pre-existing living, sanitation and
medical conditions. Therefore, we assume in addition that prior to the first case, the probability of
having Ebola events is not zero. Furthermore, according to epidemiologist practice, the transmission
rate for an epidemic disease can be modelled as follow:
βit = β0i e –kiτ (2)
Where:
ki ≥ 0 measures the efficiency of the intervention or control measures set up to limit the spread of the
epidemic disease. It represents the control factor.
84
τ
is the duration between the time the transmission rate is measured and the initial time the
intervention started: τ = t – t 0
βit represents the rate of transmission of the disease at time t in the population of country i.
β0i is a constant which represents the initial rate of transmission of the disease in country i before
any intervention.
If there is no intervention to control the disease, the control factor is 0 and the rate of transmission
in the population will stay constant over time. In this case, the rate of transmission of the disease
at any time will be βit = β0i
If the intervention is efficient, the control factor will be high, and the transmission rate will decrease
exponentially. If a country maintains its control factor constant, the transmission rate will become
zero as time increases.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Based on the discussion above, the distribution of the probability of EVD prevalence was modelled.
Ebola follows an exponential function dynamics over time with a control factor, which varies from one
country to another. For each scenario, either high or low Ebola scenario, the probability of a new Ebola
case in country i at time t is given by:
Pi0 e ki (t–ti0 ) if ti0 ≤ t ≤ t peak
Pit =
Pt
Pi0 e –ki (ti0–t) if t ≤ ti0
peak
e–ki (t–tpeak ) if t ≥ t peak
’ (3)
Where:
• ti0 is the initial date. In this study it is equal to the month the first case of Ebola occurs in country i. In the
case the country never experiences Ebola, we use the initial date observed in the nearest country with
effective Ebola cases.
• tpeak is the date of the highest probability. This report assumes that the Ebola situation will not worsen
in the future. Therefore, the highest probability is equal to the estimated probability for November 2014
reported in the World Bank report.
• ki is the control factor set up in country i to fight Ebola.
• Pi0 is the probability at the first occurrence date t_i0, of having Ebola cases during the next month.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
85
Annex 3. Modelling food security and poverty incidence
To assess the social impact of the EVD, the model focuses on food security and poverty aspects. Two
approaches are used for this purpose. The analysis of the impact of the EVD on poverty is based on the
approach developed by Son and Kakwani (2004) applied to Côte d’Ivoire by Aka and Diallo (2009). Based
on the results of the EVD impact on economic growth, we estimate a link with poverty indices starting
from the equation below:
dP
d
dG
 
 

P
G (4)
Where Pα is the FGT index, α is the mean income of the population and G is the Gini index. We assume
that the mean income of the population grows at the same rate as the GDP per capita. We also assume that,
in the short term, change in income distribution will be negligible so that the change in poverty is mainly
due to the change in the average income of the population. This therefore yields:
dP
d
 
(5)
P
 dP
d
 
ddP

PdP
dd
=   
* , the growth elasticity of poverty.
dP
where 
P
PP
It follows that:

dP
d
 
P

dPdP
d


Pα,t+1 = (1+
)P (6)
PP
 α,t
The growth elasticity is from Kouadio, Gbongué and Ouattara (2006) for Côte d’Ivoire, Boccanfuso
and Kaboré (2003) for Burkina Faso and Senegal, and Chukwu (2014) for Nigeria. For the other West
African countries, the estimate of Anyanwu (2013) for sub-Saharan Africa is used. Combining the growth
elasticity with the results of the EVD macro-economic impact model, we can estimate the poverty effect of
the epidemic.
Combining this result with that of the macro-economic model, we determine the path of food security
indicators for each of the scenarios of evolution of the epidemic. Thus, we can calculate the difference
between the path of food security indicators with and without Ebola.
For this purpose, we start from the assumption that without Ebola, food security indicators will follow
the pre-outbreak trend. Next, we determine the relationship between the food security indicators and the
growth rate of GDP per capita using an econometric model.
86
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Following Stavytsky and Prokopenko (2014), a panel data model is used to estimate the relationship
between food security and GDP per capita. A panel data regression method gives the possibility to get
robust estimates and to indicate some special features for each country. The developed fixed-effect panel
data model takes the following general form:
Yit = β1 X it + αi + μ it (7)
Where:
• αi (i = 1…n) is the unknown intercept for each country i (n entity-specific intercepts);
• Yit is the dependent variable (DV) where i = entity and t = time;
• Xit represents one independent variable (IV);
• β1 is the coefficient for that X;
• μit is the error term
The estimated model is as follows:
Lfpundnit = β 0 + β1 Lgdperit + β2 Llaborilo it + β2traderatio
dP
+∑
openess
P it

d
γi Ei + μit 
(8)
Where:
• Lfpundnit represents the log of the undernourishment rate in country i at time t;
• Lgdperit is the log of GDP per capita in country i at time t;
• trade_openess_ratioit is the trade openness ratio in country i at time t;
• Ei is the country i. Since they are binary (dummies), n-1 countries are included in the model. In this
case, we have 14 countries.
• γi is the coefficient for the binary repressors (countries).
The slope coefficient on an IV is the same from one entity to the next. The entity-specific intercepts in
[eq.7] and the binary regressors in [eq.8] have the same source: the unobserved variable Zi that varies
across countries but not over time.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
87
Annex 4. Variables and data
The main dependent variable is the real gross domestic product (GDP) per capita.
The main explanatory variables used to estimate the GDP per capita function are:
• the labour force, which is the percentage of the total population that is working;
• the savings rate, which is measured by the ratio of the public investment to GDP;
• human capital accumulation, which is measured by the rate of secondary education enrolment;
• the size of the economy, which is measured by the one-period lag level of per capita GDP;
• the openness of the country.
The prevalence probability for EVD is calculated using data of the World Health Organization (WHO) on
EVD prevalence probability for each West African country.32 To capture the country’s ability to quickly
tackle Ebola transmission, two health-related variables were used:
• improved sanitation facilities, which measures the percentage of the population using improved
sanitation facilities. This variable captures the level of risk of EVD contamination;
• health expenditures per capita (or health expenditures as a percentage of GDP), which aims at measuring
the health system financing; it represents an indicator of the country’s ability to quickly allocate sufficient
financial resources to protect the population in the case of hazard events such as EVD.
To estimate the macro-Ebola model, the main constraints are the availability of data for forecasting GDP
per capita. This constraint reduces the forecast horizon. Hence, the estimation of the impact of the EVD is
done over the period 2014-2017. For some countries (e.g. Sierra Leone, Guinea Bissau), it is not possible
to estimate the impact through to 2017 due to a lack of data.
The data used for this estimation are from the World Development Indicators (WDI) of the World Bank,
covering the period 1980-2013 for the historical data. These data allow to estimate the GDP per capita. For
the forecast of 2014-2017, this report uses the data from the forecast carried out by the IMF for each of the
15 West African countries. These data allow to calibrate the GDP per capita function and then to forecast
the impact of the EVD on GDP per capita. Three models are estimated. The first model is the case with no
Ebola, which is the baseline model. Two models are then estimated: the low case EVD model and the high
Ebola scenario model.
32
88
Benin, Burkina-Faso, Cape Verde, Niger and Togo. These five countries have no assigned probabilities for the Ebola scenario in the World Bank report. This report has assigned the probability distribution of
similar countries.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Annex 5. The two sector model
Following Cuddington and Hancock (1993a), we assume a two-sector economy with formal and informal
sectors. The formal sector is capital-intensive, whereas the informal sector is labour-intensive. Jobs in the
formal sector are preferred by workers since the formal sector wage, in the short term, is higher than in the
informal sector. Workers in the informal sector are mostly self-employed.
The formal sector
We consider a Solow growth model framework where the main production factors are capital
and labour.
Labour supply. The EVD is having a dramatic consequence on population size. We will be using
population forecast by five-year age cohort i. If Nit is the population in age cohort i at the time t, total
labour supply at time t is:
LSt = ∑ 64
i=15 pi Nit (9)
Where pi is the labour participation rate for a population cohort i.
In absence of empirical estimation of the labour force participation in the countries of our interest, we assume
that pi = 0 for i < 14 and i > 64.
What would be the effect of the EVD on the average experience level of the labour force? In the case of
EVD, the productivity of a labourer is taken to be a quadratic function of work experience. In the case
of the EVD, the productivity of an individual who has contracted the disease will be low. Indeed, with
the high death rate of the disease, the productivity of anyone who died from the EVD will be zero, while
for survivors, the productivity will be low and there is no guarantee of a return to work. In this case,
ρi = ρi + ρ2 (i–15) + ρ 3 (i–15)2 (10)
with ρ1 = 0.8, ρ 2 = 0.02 and ρ 3 = -0.0002
The production function. The formal sector production function at time t is represented by a simple
Cobb-Douglas technology function:
YFt = αF γFt EFtβF KFt1–βF (11)
Where:
F denotes the formal sector,
t the time period,
α represents a constant scale factor that will be adjusted to calibrate the model in the first year (2000),
γt is the change in technology over time,
β is the output elasticity of the labour factor, i.e. the labour share of national output,
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
89
EF is the labour demand measured in efficiency units,
KF is the capital demand measured in efficiency units.
64 ρ (1– za ) L
EFt = ∑i=1
i
it Fit (12)
Where:
ait is the proportion of the labour force age i with the EVD at time t,
LFit is the number of workers of age i at time t in the formal sector,
z is the fraction of an Ebola patient’s work years lost due to the disease. With the characteristics of the
EVD, it is natural to assume that z=1 since, unlike the HIV/AIDS, anybody with the EVD may not be
able to resume work.
Labour demand. For simplification, it is assumed that no firm discriminates with respect to age or
potential EVD status. Indeed, anyone who knows they have the EVD will not apply for a job since the
disease progresses rapidly. There is then no need to discriminate against the EVD. Hence, the production
function can be rewritten as:
1–βF ) ρt LFt ) βF KFt
YFt = αF γFt ( —
(13)
The efficiency unit of labour at time t equals a cohort-weighted average of EVD exclusive productivity factors.
dP
d
—
ρt = ∑ 64
(14)
 it)
i=15 ρi (1– za
P

It has to be noted that the labour efficiency factor (14) will change over time and as the composition of the
dP
d
 
and the EVD prevalence rate change. Both a rise in ait and the shift in the labour force
labour force
P

contribute to the decline in labour efficiency over time.
Each year, since the firm knows the unit price of labour (wt), it chooses the number of labour units to hire
to maximize its profit. Hence, the labour demand will depend on the prevailing real wage and the existing
capital stock.
.(15)
LFt = ϕFt KFt
Where: [
ρFtβF ⁄ wt
ϕFt = (αF γFt βF —
] ⁄(
1
1–βF)
It has to be noted that, given the wage and capital stock, an increase of the EVD prevalence reduces
labour productivity. The output supply function for the formal sector is determined in the usual way by
substituting labour demand from (15) into the production function (13).
90
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
The informal sector
Production function. Production in the informal sector uses labour and capital with a Cobb-Douglas
technology specification.
ρt LIt )βI KIt1–βI(16)
YIt = αI γIt (—
Where:
dP
d
—
ρt = ∑ 65
(17)
 it)
i=1 ρi (1– za
P

The informal sector is assumed to be more labour-intensive that the formal sector (βI > βF ).
The behaviour of informal firms is different from the formal sector. It is assumed that total income is
divided among all informal sector workers. Hence, each worker receives:
yIt =
YIt
⁄ LIt = αI γIt —ρtβI ( KIt ⁄LIt )1–βI
(18)
This assumption is made since a large share of informal production is produced by family enterprises
whose family members share household output.
Capital accumulation
In an epidemic situation, the government will increase medical expenditures by cutting domestic savings
or by receiving other financial support. Total domestic savings in year t, St, and the capital accumulation
Kt have the following expressions:
St = sYt — xHt
and Kt = (s + s*)Yt — xHt + (1–θ) Kt–1
(19)
Where:
s
is the domestic savings rate in an Ebola-free situation,
Ht is the total annual cost needed to treat Ebola,
x is the fraction of Ht financed out of saving and
θ is the depreciation rate.
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
91
Annex 6: Distribution of Ebola virus disease prevalence probability for West Africa countries, 2013-2017
92
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Annex 6: Distribution of Ebola virus disease prevalence probability for West Africa countries, 2013-2017 (cont.)
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
93
Annex 7: Macro model estimation results
Dependent variables: GDP per capita, US$ constant price
No Ebola
Low Ebola
High Ebola
L.(GDP per capita)
0.824***
(6.396)
0.867***
(6.650)
0.854***
(8.933)
L2.(GDP per capita)
0.173
(1.441)
0.199*
(1.797)
0.225***
(2.662)
Log (total labour force)
-19.67
(-0.738)
-18.04
(-1.234)
-14.09
(-0.980)
Education
1.602*
(1.688)
0.0776
(0.0591)
0.122
(0.113)
Openness
0.965
(0.847)
-0.711
(-0.395)
-0.658
(-0.466)
Account balance as % of GDP
1.330
(1.078)
1.086
(1.240)
0.854
(1.166)
Low Ebola
Log (Low Ebola probability)
-50.73
(-0.398)
Log (Openness*(Low Ebola probability)
Log (High Ebola probability)
51.38
(0.407)
Log (Openness*(High Ebola probability)
40.92
(0.433)
The three epicentre countries
-42.14
(-0.867)
-33.51
(-0.691)
215.5
(0.500)
120.5
(0.219)
80.58
(0.196)
Observations
33
33
33
Group minimum
1
1
1
Group maximum
6
6
6
Group average
2.538
2.538
2.538
chi2
4.051
22.773
21.430
pvalue
–
–
–
AR(1)
-1.097
-1.176
-1.285
AR(2)
0.902
0.890
0.868
Constant
z-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.1
94
-40.67
(-0.428)
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
Annex 8: United Nations Development Group – Western and Central Africa (UNDG-WCA) Regional Directors Team (RDT)
Agency
Full Name
Title or Designation
Programme (UNDP), Chair
Abdoulaye Mar Dieye
RBA Director and RDT Chair
United Nations Population Funds
(UNFPA)
Mr. Benoit Kalasa
Regional Director
United Nations Children Funds
(UNICEF)
Mr. Manuel Fontaine
Regional Director
World Food Programme
(WFP)
Ms. Denise Brown
Regional Director
International Labor Organization
(ILO)
Mr. Aeneas Chapinga Chuma
Regional Director
Mr. François Murangira
Director, Sahel Region & West Africa
Vacant
Director for Central Africa
Dr. Mamadou Diallo
Regional Director
United Nations Educational, Scientific and Cultural Organization
(UNESCO)
Ms. Ann Therese Ndong-Jatta
Regional Director
United Nations Women Organization
(UNWOMEN)
Ms. Joséphine Odera
Regional Director
United Nations Office for the Coordination of Humanitarian Affairs
(UNOCHA)
Ms. Allegra Baiocchi
Head of Regional Office WCA
United Nations Office for Drugs and Crime
(UNODC)
Mr. Pierre Lapaque
Regional Representative, WCA
United Nations Industrial Development Organization
(UNIDO)
Mr. Patrick Kormawa
Regional Director
World Health Organization
(WHO)
Dr. Matshidiso Moeti
Regional Director
Dr. Djamila Khady Cabral
Coordinator, WHO-IST for W. Africa
Office of the High Commissioner for Human Rights
(OHCHR)
Mr. Andrea Ori
Regional Representative for W. Africa
Mr. Ahowanou Agbessi
Regional Representative for C. Africa
Food and Agriculture Organization
(FAO)
Mr. Thiombiano Lamourdia
Deputy Regional Rep for Africa
Mr. Vincent Martin
Resident Representative for Senegal &
Head of regional office for resilience,
urgency and rehabilitation
International Organization for Migration
(IOM)
Ms. Carmela Godeau
Regional Director, WCA
United Nations Economic Commission for Africa/Sub-Regional Office)
(UNECA/SRO)
Mr. Dimitri Sanga
Director for West Africa
United Nations Office for Project Services
(UNOPS
Mr. Garry Conille
Director for Africa
Mr. Pierre Jullien
Regional Director, WCA
United Nations Environment Programme
(UNEP)
Mr. Mounkaila Goumandakoye
Regional Director
United Nations Development Group for Western and Central Africa
(UNDG-WCA)
Mr. Mensah Aluka
Regional Coordination Specialist,
Head of UNDG-WCA Secretariat
Ms. Alizeta (Lizet) Simboro Sawadogo
Regional Coordination Associate
Joint United Nations Programme Against HIV/AIDS
(UNAIDS)
United Nations High Commissioner for Refugees
(UNHCR)
SOCIO-ECONOMIC IMPACT OF EBOLA VIRUS DISEASE IN WEST AFRICAN COUNTRIES
95
Vendors struggle with plummeting sales and rising cost of transporting goods to the market in West Point after the Ebola Outbreak and qurantine took effect in Liberia. Photo by UNDP/Morgana Wingard
96
97